diff --git a/environments/ai_vs_ai/ml-agents b/environments/ai_vs_ai/ml-agents
--- a/environments/ai_vs_ai/ml-agents
+++ b/environments/ai_vs_ai/ml-agents
@@ -1 +1 @@
-Subproject commit 8bcedabd808ffb7097f88b800fc92dea82dfd610
+Subproject commit 8bcedabd808ffb7097f88b800fc92dea82dfd610-dirty
diff --git a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/0.monitor.csv b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/0.monitor.csv
index fb7bd62..29c4e43 100644
--- a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/0.monitor.csv
+++ b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/0.monitor.csv
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diff --git a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/best_model.zip b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/best_model.zip
index ade47c0..3e11ac4 100644
Binary files a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/best_model.zip and b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/best_model.zip differ
diff --git a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/evaluations.npz b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/evaluations.npz
index c09117b..be110df 100644
Binary files a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/evaluations.npz and b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/evaluations.npz differ
diff --git a/environments/sample_factory/doom_healthgathering.ipynb b/environments/sample_factory/doom_healthgathering.ipynb
index a7be1b5..84985a6 100644
--- a/environments/sample_factory/doom_healthgathering.ipynb
+++ b/environments/sample_factory/doom_healthgathering.ipynb
@@ -1451,7 +1451,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1465,7 +1465,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.8.10"
   },
   "vscode": {
    "interpreter": {
diff --git a/environments/sample_factory/train_dir/default_experiment/README.md b/environments/sample_factory/train_dir/default_experiment/README.md
index 67b28b9..3d45e31 100644
--- a/environments/sample_factory/train_dir/default_experiment/README.md
+++ b/environments/sample_factory/train_dir/default_experiment/README.md
@@ -11,16 +11,16 @@ model-index:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
-      name: doom_health_gathering_supreme
-      type: doom_health_gathering_supreme
+      name: doom_defend_the_center
+      type: doom_defend_the_center
     metrics:
     - type: mean_reward
-      value: 9.02 +/- 3.07
+      value: 10.60 +/- 1.20
       name: mean_reward
       verified: false
 ---
 
-A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
+A(n) **APPO** model trained on the **doom_defend_the_center** environment.
 
 This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
 Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
@@ -30,7 +30,7 @@ Documentation for how to use Sample-Factory can be found at https://www.samplefa
 
 After installing Sample-Factory, download the model with:
 ```
-python -m sample_factory.huggingface.load_from_hub -r MattStammers/rl_course_vizdoom_health_gathering_supreme
+python -m sample_factory.huggingface.load_from_hub -r MattStammers/_vizdoom_defend_the_center
 ```
 
     
@@ -38,7 +38,7 @@ python -m sample_factory.huggingface.load_from_hub -r MattStammers/rl_course_viz
 
 To run the model after download, use the `enjoy` script corresponding to this environment:
 ```
-python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
+python -m <path.to.enjoy.module> --algo=APPO --env=doom_defend_the_center --train_dir=./train_dir --experiment=_vizdoom_defend_the_center
 ```
 
 
@@ -49,7 +49,7 @@ See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
 
 To continue training with this model, use the `train` script corresponding to this environment:
 ```
-python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
+python -m <path.to.train.module> --algo=APPO --env=doom_defend_the_center --train_dir=./train_dir --experiment=_vizdoom_defend_the_center --restart_behavior=resume --train_for_env_steps=10000000000
 ```
 
 Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
diff --git a/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000742_3039232.pth b/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000742_3039232.pth
deleted file mode 100644
index 828ec68..0000000
Binary files a/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000742_3039232.pth and /dev/null differ
diff --git a/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth b/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth
deleted file mode 100644
index 47ca479..0000000
Binary files a/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth and /dev/null differ
diff --git a/environments/sample_factory/train_dir/default_experiment/config.json b/environments/sample_factory/train_dir/default_experiment/config.json
index 0b054da..f3f5503 100644
--- a/environments/sample_factory/train_dir/default_experiment/config.json
+++ b/environments/sample_factory/train_dir/default_experiment/config.json
@@ -1,7 +1,7 @@
 {
   "help": false,
   "algo": "APPO",
-  "env": "doom_health_gathering_supreme",
+  "env": "doom_defend_the_line",
   "experiment": "default_experiment",
   "train_dir": "/home/cogstack/Documents/optuna/environments/sample_factory/train_dir",
   "restart_behavior": "resume",
diff --git a/environments/sample_factory/train_dir/default_experiment/git.diff b/environments/sample_factory/train_dir/default_experiment/git.diff
index c5278a7..9b23ce6 100644
--- a/environments/sample_factory/train_dir/default_experiment/git.diff
+++ b/environments/sample_factory/train_dir/default_experiment/git.diff
@@ -1,454 +0,0 @@
-diff --git a/environments/ai_vs_ai/ml-agents b/environments/ai_vs_ai/ml-agents
---- a/environments/ai_vs_ai/ml-agents
-+++ b/environments/ai_vs_ai/ml-agents
-@@ -1 +1 @@
--Subproject commit 8bcedabd808ffb7097f88b800fc92dea82dfd610
-+Subproject commit 8bcedabd808ffb7097f88b800fc92dea82dfd610-dirty
-diff --git a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/0.monitor.csv b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/0.monitor.csv
-index dfafaed..01a0b2a 100644
---- a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/0.monitor.csv
-+++ b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/0.monitor.csv
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-diff --git a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/evaluations.npz b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/evaluations.npz
-index f97b7da..b1d2a74 100644
-Binary files a/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/evaluations.npz and b/environments/atari/model/qrdqn/QbertNoFrameskip-v4_6/evaluations.npz differ
-diff --git a/environments/sample_factory/doom_healthgathering.ipynb b/environments/sample_factory/doom_healthgathering.ipynb
-index 225e545..d46549e 100644
---- a/environments/sample_factory/doom_healthgathering.ipynb
-+++ b/environments/sample_factory/doom_healthgathering.ipynb
-@@ -1,15 +1,233 @@
- {
-  "cells": [
-   {
--   "attachments": {},
-    "cell_type": "markdown",
-    "metadata": {},
-    "source": [
--    "# PPO From Scratch\n",
-+    "# Doom Health Gathering\n",
-     "\n",
-     "By Matt Stammers"
-    ]
-   },
-+  {
-+   "cell_type": "code",
-+   "execution_count": 1,
-+   "metadata": {},
-+   "outputs": [
-+    {
-+     "data": {
-+      "text/html": [
-+       "<video width=\"640\" height=\"480\" controls>\n",
-+       "  <source src=\"https://huggingface.co/edbeeching/doom_health_gathering_supreme_3333/resolve/main/replay.mp4\"\n",
-+       "  type=\"video/mp4\">Your browser does not support the video tag.</video>"
-+      ],
-+      "text/plain": [
-+       "<IPython.core.display.HTML object>"
-+      ]
-+     },
-+     "execution_count": 1,
-+     "metadata": {},
-+     "output_type": "execute_result"
-+    }
-+   ],
-+   "source": [
-+    "from IPython.display import HTML\n",
-+    "\n",
-+    "HTML(\n",
-+    "    \"\"\"<video width=\"640\" height=\"480\" controls>\n",
-+    "  <source src=\"https://huggingface.co/edbeeching/doom_health_gathering_supreme_3333/resolve/main/replay.mp4\"\n",
-+    "  type=\"video/mp4\">Your browser does not support the video tag.</video>\"\"\"\n",
-+    ")"
-+   ]
-+  },
-+  {
-+   "cell_type": "code",
-+   "execution_count": 2,
-+   "metadata": {},
-+   "outputs": [
-+    {
-+     "name": "stdout",
-+     "output_type": "stream",
-+     "text": [
-+      "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\n",
-+      "E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\n",
-+      "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\n",
-+      "E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\n",
-+      "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\n",
-+      "E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\n"
-+     ]
-+    }
-+   ],
-+   "source": [
-+    "# Install ViZDoom deps from\n",
-+    "# https://github.com/mwydmuch/ViZDoom/blob/master/doc/Building.md#-linux\n",
-+    "\n",
-+    "!apt-get install build-essential zlib1g-dev libsdl2-dev libjpeg-dev \\\n",
-+    "nasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \\\n",
-+    "libopenal-dev timidity libwildmidi-dev unzip ffmpeg\n",
-+    "\n",
-+    "# Boost libraries\n",
-+    "!apt-get install libboost-all-dev\n",
-+    "\n",
-+    "# Lua binding dependencies\n",
-+    "!apt-get install liblua5.1-dev"
-+   ]
-+  },
-+  {
-+   "cell_type": "code",
-+   "execution_count": 3,
-+   "metadata": {},
-+   "outputs": [
-+    {
-+     "name": "stdout",
-+     "output_type": "stream",
-+     "text": [
-+      "Requirement already satisfied: sample-factory in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (2.1.1)\n",
-+      "Requirement already satisfied: numpy<2.0,>=1.18.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (1.21.2)\n",
-+      "Requirement already satisfied: torch!=1.13.0,<3.0,>=1.9 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (1.11.0)\n",
-+      "Requirement already satisfied: gymnasium<1.0,>=0.27 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (0.29.1)\n",
-+      "Requirement already satisfied: pyglet in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (2.0.9)\n",
-+      "Requirement already satisfied: tensorboard>=1.15.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (2.12.3)\n",
-+      "Requirement already satisfied: tensorboardx>=2.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (2.6.2.2)\n",
-+      "Requirement already satisfied: psutil>=5.7.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (5.9.5)\n",
-+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (3.2.0)\n",
-+      "Requirement already satisfied: colorlog in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (6.7.0)\n",
-+      "Requirement already satisfied: signal-slot-mp<2.0,>=1.0.3 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (1.0.5)\n",
-+      "Requirement already satisfied: filelock in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (3.8.2)\n",
-+      "Requirement already satisfied: opencv-python in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (4.8.0.76)\n",
-+      "Requirement already satisfied: wandb>=0.12.9 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (0.15.10)\n",
-+      "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from sample-factory) (0.16.4)\n",
-+      "Requirement already satisfied: cloudpickle>=1.2.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from gymnasium<1.0,>=0.27->sample-factory) (2.2.1)\n",
-+      "Requirement already satisfied: typing-extensions>=4.3.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from gymnasium<1.0,>=0.27->sample-factory) (4.3.0)\n",
-+      "Requirement already satisfied: farama-notifications>=0.0.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from gymnasium<1.0,>=0.27->sample-factory) (0.0.4)\n",
-+      "Requirement already satisfied: fsspec in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.10.0->sample-factory) (2023.9.0)\n",
-+      "Requirement already satisfied: requests in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.10.0->sample-factory) (2.28.2)\n",
-+      "Requirement already satisfied: tqdm>=4.42.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.10.0->sample-factory) (4.66.1)\n",
-+      "Requirement already satisfied: pyyaml>=5.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.10.0->sample-factory) (6.0.1)\n",
-+      "Requirement already satisfied: packaging>=20.9 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.10.0->sample-factory) (23.0)\n",
-+      "Requirement already satisfied: faster-fifo<2.0,>=1.4.4 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from signal-slot-mp<2.0,>=1.0.3->sample-factory) (1.4.5)\n",
-+      "Requirement already satisfied: absl-py>=0.4 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (1.4.0)\n",
-+      "Requirement already satisfied: grpcio>=1.48.2 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (1.56.0)\n",
-+      "Requirement already satisfied: google-auth<3,>=1.6.3 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (2.21.0)\n",
-+      "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (1.0.0)\n",
-+      "Requirement already satisfied: markdown>=2.6.8 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (3.4.3)\n",
-+      "Requirement already satisfied: protobuf>=3.19.6 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (4.24.3)\n",
-+      "Requirement already satisfied: setuptools>=41.0.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (63.2.0)\n",
-+      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (0.7.1)\n",
-+      "Requirement already satisfied: werkzeug>=1.0.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (2.3.6)\n",
-+      "Requirement already satisfied: wheel>=0.26 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from tensorboard>=1.15.0->sample-factory) (0.40.0)\n",
-+      "Requirement already satisfied: Click!=8.0.0,>=7.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from wandb>=0.12.9->sample-factory) (8.1.3)\n",
-+      "Requirement already satisfied: GitPython!=3.1.29,>=1.0.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from wandb>=0.12.9->sample-factory) (3.1.35)\n",
-+      "Requirement already satisfied: sentry-sdk>=1.0.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from wandb>=0.12.9->sample-factory) (1.30.0)\n",
-+      "Requirement already satisfied: docker-pycreds>=0.4.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from wandb>=0.12.9->sample-factory) (0.4.0)\n",
-+      "Requirement already satisfied: pathtools in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from wandb>=0.12.9->sample-factory) (0.1.2)\n",
-+      "Requirement already satisfied: setproctitle in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from wandb>=0.12.9->sample-factory) (1.3.2)\n",
-+      "Requirement already satisfied: appdirs>=1.4.3 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from wandb>=0.12.9->sample-factory) (1.4.4)\n",
-+      "Requirement already satisfied: six>=1.4.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from docker-pycreds>=0.4.0->wandb>=0.12.9->sample-factory) (1.16.0)\n",
-+      "Requirement already satisfied: cython>=0.29 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from faster-fifo<2.0,>=1.4.4->signal-slot-mp<2.0,>=1.0.3->sample-factory) (3.0.2)\n",
-+      "Requirement already satisfied: gitdb<5,>=4.0.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from GitPython!=3.1.29,>=1.0.0->wandb>=0.12.9->sample-factory) (4.0.10)\n",
-+      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard>=1.15.0->sample-factory) (5.3.1)\n",
-+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard>=1.15.0->sample-factory) (0.3.0)\n",
-+      "Requirement already satisfied: rsa<5,>=3.1.4 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard>=1.15.0->sample-factory) (4.9)\n",
-+      "Requirement already satisfied: urllib3<2.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard>=1.15.0->sample-factory) (1.26.14)\n",
-+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard>=1.15.0->sample-factory) (1.3.1)\n",
-+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from requests->huggingface-hub<1.0,>=0.10.0->sample-factory) (3.0.1)\n",
-+      "Requirement already satisfied: idna<4,>=2.5 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from requests->huggingface-hub<1.0,>=0.10.0->sample-factory) (3.4)\n",
-+      "Requirement already satisfied: certifi>=2017.4.17 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from requests->huggingface-hub<1.0,>=0.10.0->sample-factory) (2022.12.7)\n",
-+      "Requirement already satisfied: MarkupSafe>=2.1.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard>=1.15.0->sample-factory) (2.1.3)\n",
-+      "Requirement already satisfied: smmap<6,>=3.0.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb>=0.12.9->sample-factory) (5.0.0)\n",
-+      "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=1.15.0->sample-factory) (0.5.0)\n",
-+      "Requirement already satisfied: oauthlib>=3.0.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard>=1.15.0->sample-factory) (3.2.2)\n"
-+     ]
-+    }
-+   ],
-+   "source": [
-+    "!pip install sample-factory"
-+   ]
-+  },
-+  {
-+   "cell_type": "code",
-+   "execution_count": 4,
-+   "metadata": {},
-+   "outputs": [
-+    {
-+     "name": "stdout",
-+     "output_type": "stream",
-+     "text": [
-+      "Requirement already satisfied: vizdoom in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (1.2.0)\n",
-+      "Requirement already satisfied: numpy in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from vizdoom) (1.21.2)\n",
-+      "Requirement already satisfied: gymnasium>=0.28.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from vizdoom) (0.29.1)\n",
-+      "Requirement already satisfied: pygame>=2.1.3 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from vizdoom) (2.5.1)\n",
-+      "Requirement already satisfied: cloudpickle>=1.2.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from gymnasium>=0.28.0->vizdoom) (2.2.1)\n",
-+      "Requirement already satisfied: typing-extensions>=4.3.0 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from gymnasium>=0.28.0->vizdoom) (4.3.0)\n",
-+      "Requirement already satisfied: farama-notifications>=0.0.1 in /home/cogstack/.pyenv/versions/3.10.6/lib/python3.10/site-packages (from gymnasium>=0.28.0->vizdoom) (0.0.4)\n"
-+     ]
-+    }
-+   ],
-+   "source": [
-+    "!pip install vizdoom"
-+   ]
-+  },
-+  {
-+   "cell_type": "code",
-+   "execution_count": 7,
-+   "metadata": {},
-+   "outputs": [
-+    {
-+     "ename": "ModuleNotFoundError",
-+     "evalue": "No module named 'sample_factory'",
-+     "output_type": "error",
-+     "traceback": [
-+      "\u001b[0;31m-------------------------------------------------------\u001b[0m",
-+      "\u001b[0;31mModuleNotFoundError\u001b[0m   Traceback (most recent call last)",
-+      "\u001b[0;32m<ipython-input-7-4f2201ebf68f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfunctools\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msample_factory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mglobal_model_factory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msample_factory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marguments\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mparse_full_cfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_sf_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msample_factory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menvs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv_utils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mregister_env\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sample_factory'"
-+     ]
-+    }
-+   ],
-+   "source": [
-+    "import functools\n",
-+    "\n",
-+    "from sample_factory.algo.utils.context import global_model_factory\n",
-+    "from sample_factory.cfg.arguments import parse_full_cfg, parse_sf_args\n",
-+    "from sample_factory.envs.env_utils import register_env\n",
-+    "from sample_factory.train import run_rl\n",
-+    "\n",
-+    "from sf_examples.vizdoom.doom.doom_model import make_vizdoom_encoder\n",
-+    "from sf_examples.vizdoom.doom.doom_params import add_doom_env_args, doom_override_defaults\n",
-+    "from sf_examples.vizdoom.doom.doom_utils import DOOM_ENVS, make_doom_env_from_spec\n",
-+    "\n",
-+    "\n",
-+    "# Registers all the ViZDoom environments\n",
-+    "def register_vizdoom_envs():\n",
-+    "    for env_spec in DOOM_ENVS:\n",
-+    "        make_env_func = functools.partial(make_doom_env_from_spec, env_spec)\n",
-+    "        register_env(env_spec.name, make_env_func)\n",
-+    "\n",
-+    "\n",
-+    "# Sample Factory allows the registration of a custom Neural Network architecture\n",
-+    "# See https://github.com/alex-petrenko/sample-factory/blob/master/sf_examples/vizdoom/doom/doom_model.py for more details\n",
-+    "def register_vizdoom_models():\n",
-+    "    global_model_factory().register_encoder_factory(make_vizdoom_encoder)\n",
-+    "\n",
-+    "\n",
-+    "def register_vizdoom_components():\n",
-+    "    register_vizdoom_envs()\n",
-+    "    register_vizdoom_models()\n",
-+    "\n",
-+    "\n",
-+    "# parse the command line args and create a config\n",
-+    "def parse_vizdoom_cfg(argv=None, evaluation=False):\n",
-+    "    parser, _ = parse_sf_args(argv=argv, evaluation=evaluation)\n",
-+    "    # parameters specific to Doom envs\n",
-+    "    add_doom_env_args(parser)\n",
-+    "    # override Doom default values for algo parameters\n",
-+    "    doom_override_defaults(parser)\n",
-+    "    # second parsing pass yields the final configuration\n",
-+    "    final_cfg = parse_full_cfg(parser, argv)\n",
-+    "    return final_cfg"
-+   ]
-+  },
-   {
-    "cell_type": "code",
-    "execution_count": null,
-@@ -25,10 +243,17 @@
-    "name": "python3"
-   },
-   "language_info": {
-+   "codemirror_mode": {
-+    "name": "ipython",
-+    "version": 3
-+   },
-+   "file_extension": ".py",
-+   "mimetype": "text/x-python",
-    "name": "python",
--   "version": "3.10.6 (main, Aug 10 2022, 05:05:41) [GCC 9.4.0]"
-+   "nbconvert_exporter": "python",
-+   "pygments_lexer": "ipython3",
-+   "version": "3.8.10"
-   },
--  "orig_nbformat": 4,
-   "vscode": {
-    "interpreter": {
-     "hash": "05ac9c948f4aa7087e84636ba0d4c25205962e0cead0fef8dfbcca0508e21804"
-@@ -36,5 +261,5 @@
-   }
-  },
-  "nbformat": 4,
-- "nbformat_minor": 2
-+ "nbformat_minor": 4
- }
-diff --git a/environments/unity/ml-agents b/environments/unity/ml-agents
---- a/environments/unity/ml-agents
-+++ b/environments/unity/ml-agents
-@@ -1 +1 @@
--Subproject commit 8bcedabd808ffb7097f88b800fc92dea82dfd610
-+Subproject commit 8bcedabd808ffb7097f88b800fc92dea82dfd610-dirty
diff --git a/environments/sample_factory/train_dir/default_experiment/replay.mp4 b/environments/sample_factory/train_dir/default_experiment/replay.mp4
index 34778cd..98a7f57 100644
Binary files a/environments/sample_factory/train_dir/default_experiment/replay.mp4 and b/environments/sample_factory/train_dir/default_experiment/replay.mp4 differ
diff --git a/environments/sample_factory/train_dir/default_experiment/sf_log.txt b/environments/sample_factory/train_dir/default_experiment/sf_log.txt
index 7ce93a9..8a2df02 100644
--- a/environments/sample_factory/train_dir/default_experiment/sf_log.txt
+++ b/environments/sample_factory/train_dir/default_experiment/sf_log.txt
@@ -756,3 +756,6554 @@ main_loop: 308.5378
 [2023-09-10 16:53:35,909][59193] Avg episode reward: 20.024, avg true_objective: 9.024
 [2023-09-10 16:54:01,506][59193] Replay video saved to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/replay.mp4!
 [2023-09-10 16:54:06,213][59193] The model has been pushed to https://huggingface.co/MattStammers/rl_course_vizdoom_health_gathering_supreme
+[2023-09-11 21:12:16,824][88175] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-11 21:12:17,292][88175] Rollout worker 0 uses device cpu
+[2023-09-11 21:12:17,293][88175] Rollout worker 1 uses device cpu
+[2023-09-11 21:12:17,294][88175] Rollout worker 2 uses device cpu
+[2023-09-11 21:12:17,294][88175] Rollout worker 3 uses device cpu
+[2023-09-11 21:12:17,295][88175] Rollout worker 4 uses device cpu
+[2023-09-11 21:12:17,296][88175] Rollout worker 5 uses device cpu
+[2023-09-11 21:12:17,297][88175] Rollout worker 6 uses device cpu
+[2023-09-11 21:12:17,299][88175] Rollout worker 7 uses device cpu
+[2023-09-11 21:12:17,471][88175] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:12:17,472][88175] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-11 21:12:17,638][88175] Starting all processes...
+[2023-09-11 21:12:17,640][88175] Starting process learner_proc0
+[2023-09-11 21:12:19,667][88175] Starting all processes...
+[2023-09-11 21:12:19,669][102594] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:12:19,669][102594] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-11 21:12:19,673][88175] Starting process inference_proc0-0
+[2023-09-11 21:12:19,673][88175] Starting process rollout_proc0
+[2023-09-11 21:12:19,674][88175] Starting process rollout_proc1
+[2023-09-11 21:12:19,674][88175] Starting process rollout_proc2
+[2023-09-11 21:12:19,707][102594] Num visible devices: 1
+[2023-09-11 21:12:19,674][88175] Starting process rollout_proc3
+[2023-09-11 21:12:19,675][88175] Starting process rollout_proc4
+[2023-09-11 21:12:19,676][88175] Starting process rollout_proc5
+[2023-09-11 21:12:19,755][102594] Starting seed is not provided
+[2023-09-11 21:12:19,756][102594] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:12:19,756][102594] Initializing actor-critic model on device cuda:0
+[2023-09-11 21:12:19,676][88175] Starting process rollout_proc6
+[2023-09-11 21:12:19,756][102594] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-11 21:12:19,757][102594] RunningMeanStd input shape: (1,)
+[2023-09-11 21:12:19,677][88175] Starting process rollout_proc7
+[2023-09-11 21:12:19,778][102594] ConvEncoder: input_channels=3
+[2023-09-11 21:12:20,034][102594] Conv encoder output size: 512
+[2023-09-11 21:12:20,035][102594] Policy head output size: 512
+[2023-09-11 21:12:20,054][102594] Created Actor Critic model with architecture:
+[2023-09-11 21:12:20,054][102594] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(512, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=5, bias=True)
+  )
+)
+[2023-09-11 21:12:21,463][102594] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-11 21:12:21,464][102594] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
+[2023-09-11 21:12:21,517][102594] Loading model from checkpoint
+[2023-09-11 21:12:21,524][102594] Loaded experiment state at self.train_step=978, self.env_steps=4005888
+[2023-09-11 21:12:21,525][102594] Initialized policy 0 weights for model version 978
+[2023-09-11 21:12:21,527][102594] LearnerWorker_p0 finished initialization!
+[2023-09-11 21:12:21,528][102594] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:12:22,205][103016] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-11 21:12:22,245][103019] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-11 21:12:22,337][103013] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-11 21:12:22,341][103021] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-11 21:12:22,383][103018] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-11 21:12:22,383][103020] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-11 21:12:22,392][103015] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-11 21:12:22,463][102980] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-11 21:12:22,568][102981] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:12:22,568][102981] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-11 21:12:22,590][102981] Num visible devices: 1
+[2023-09-11 21:12:23,327][102981] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-11 21:12:23,328][102981] RunningMeanStd input shape: (1,)
+[2023-09-11 21:12:23,341][102981] ConvEncoder: input_channels=3
+[2023-09-11 21:12:23,447][102981] Conv encoder output size: 512
+[2023-09-11 21:12:23,447][102981] Policy head output size: 512
+[2023-09-11 21:12:23,809][88175] Inference worker 0-0 is ready!
+[2023-09-11 21:12:23,810][88175] All inference workers are ready! Signal rollout workers to start!
+[2023-09-11 21:12:23,881][103013] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:12:23,882][103021] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:12:23,908][102980] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:12:23,922][103016] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:12:23,928][103015] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:12:23,931][103018] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:12:23,932][103019] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:12:23,941][88175] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 4005888. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
+[2023-09-11 21:12:23,950][103020] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:12:24,271][103015] Decorrelating experience for 0 frames...
+[2023-09-11 21:12:24,286][103016] Decorrelating experience for 0 frames...
+[2023-09-11 21:12:24,302][102980] Decorrelating experience for 0 frames...
+[2023-09-11 21:12:24,311][103018] Decorrelating experience for 0 frames...
+[2023-09-11 21:12:24,381][103013] Decorrelating experience for 0 frames...
+[2023-09-11 21:12:24,413][103019] Decorrelating experience for 0 frames...
+[2023-09-11 21:12:24,582][103015] Decorrelating experience for 32 frames...
+[2023-09-11 21:12:24,739][103019] Decorrelating experience for 32 frames...
+[2023-09-11 21:12:24,739][103021] Decorrelating experience for 0 frames...
+[2023-09-11 21:12:24,741][103016] Decorrelating experience for 32 frames...
+[2023-09-11 21:12:24,758][102980] Decorrelating experience for 32 frames...
+[2023-09-11 21:12:24,764][103018] Decorrelating experience for 32 frames...
+[2023-09-11 21:12:24,892][103013] Decorrelating experience for 32 frames...
+[2023-09-11 21:12:25,101][103021] Decorrelating experience for 32 frames...
+[2023-09-11 21:12:25,131][103020] Decorrelating experience for 0 frames...
+[2023-09-11 21:12:25,238][103016] Decorrelating experience for 64 frames...
+[2023-09-11 21:12:25,252][103015] Decorrelating experience for 64 frames...
+[2023-09-11 21:12:25,281][103013] Decorrelating experience for 64 frames...
+[2023-09-11 21:12:25,455][103018] Decorrelating experience for 64 frames...
+[2023-09-11 21:12:25,462][103019] Decorrelating experience for 64 frames...
+[2023-09-11 21:12:25,463][103020] Decorrelating experience for 32 frames...
+[2023-09-11 21:12:25,533][103021] Decorrelating experience for 64 frames...
+[2023-09-11 21:12:25,641][103015] Decorrelating experience for 96 frames...
+[2023-09-11 21:12:25,695][103013] Decorrelating experience for 96 frames...
+[2023-09-11 21:12:25,794][103016] Decorrelating experience for 96 frames...
+[2023-09-11 21:12:25,846][102980] Decorrelating experience for 64 frames...
+[2023-09-11 21:12:25,852][103019] Decorrelating experience for 96 frames...
+[2023-09-11 21:12:25,902][103020] Decorrelating experience for 64 frames...
+[2023-09-11 21:12:26,097][103018] Decorrelating experience for 96 frames...
+[2023-09-11 21:12:26,247][103020] Decorrelating experience for 96 frames...
+[2023-09-11 21:12:26,255][103021] Decorrelating experience for 96 frames...
+[2023-09-11 21:12:26,429][102980] Decorrelating experience for 96 frames...
+[2023-09-11 21:12:27,282][102594] Signal inference workers to stop experience collection...
+[2023-09-11 21:12:27,291][102981] InferenceWorker_p0-w0: stopping experience collection
+[2023-09-11 21:12:28,941][88175] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 4005888. Throughput: 0: 305.6. Samples: 1528. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
+[2023-09-11 21:12:28,942][88175] Avg episode reward: [(0, '-0.969')]
+[2023-09-11 21:12:31,881][102594] Signal inference workers to resume experience collection...
+[2023-09-11 21:12:31,882][102981] InferenceWorker_p0-w0: resuming experience collection
+[2023-09-11 21:12:31,883][102594] Stopping Batcher_0...
+[2023-09-11 21:12:31,884][102594] Loop batcher_evt_loop terminating...
+[2023-09-11 21:12:31,890][88175] Component Batcher_0 stopped!
+[2023-09-11 21:12:31,898][103013] Stopping RolloutWorker_w1...
+[2023-09-11 21:12:31,899][103013] Loop rollout_proc1_evt_loop terminating...
+[2023-09-11 21:12:31,899][103020] Stopping RolloutWorker_w6...
+[2023-09-11 21:12:31,899][103020] Loop rollout_proc6_evt_loop terminating...
+[2023-09-11 21:12:31,899][103016] Stopping RolloutWorker_w3...
+[2023-09-11 21:12:31,900][103016] Loop rollout_proc3_evt_loop terminating...
+[2023-09-11 21:12:31,898][88175] Component RolloutWorker_w1 stopped!
+[2023-09-11 21:12:31,900][103015] Stopping RolloutWorker_w2...
+[2023-09-11 21:12:31,900][103021] Stopping RolloutWorker_w7...
+[2023-09-11 21:12:31,900][103015] Loop rollout_proc2_evt_loop terminating...
+[2023-09-11 21:12:31,901][103021] Loop rollout_proc7_evt_loop terminating...
+[2023-09-11 21:12:31,901][103019] Stopping RolloutWorker_w5...
+[2023-09-11 21:12:31,901][103019] Loop rollout_proc5_evt_loop terminating...
+[2023-09-11 21:12:31,901][88175] Component RolloutWorker_w6 stopped!
+[2023-09-11 21:12:31,902][102980] Stopping RolloutWorker_w0...
+[2023-09-11 21:12:31,902][102980] Loop rollout_proc0_evt_loop terminating...
+[2023-09-11 21:12:31,902][103018] Stopping RolloutWorker_w4...
+[2023-09-11 21:12:31,903][103018] Loop rollout_proc4_evt_loop terminating...
+[2023-09-11 21:12:31,902][88175] Component RolloutWorker_w3 stopped!
+[2023-09-11 21:12:31,903][88175] Component RolloutWorker_w2 stopped!
+[2023-09-11 21:12:31,905][88175] Component RolloutWorker_w7 stopped!
+[2023-09-11 21:12:31,906][88175] Component RolloutWorker_w5 stopped!
+[2023-09-11 21:12:31,906][88175] Component RolloutWorker_w0 stopped!
+[2023-09-11 21:12:31,907][88175] Component RolloutWorker_w4 stopped!
+[2023-09-11 21:12:32,416][102981] Weights refcount: 2 0
+[2023-09-11 21:12:32,417][102981] Stopping InferenceWorker_p0-w0...
+[2023-09-11 21:12:32,417][102981] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-11 21:12:32,417][88175] Component InferenceWorker_p0-w0 stopped!
+[2023-09-11 21:12:33,018][102594] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000980_4014080.pth...
+[2023-09-11 21:12:33,067][102594] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000742_3039232.pth
+[2023-09-11 21:12:33,074][102594] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000980_4014080.pth...
+[2023-09-11 21:12:33,138][102594] Stopping LearnerWorker_p0...
+[2023-09-11 21:12:33,138][102594] Loop learner_proc0_evt_loop terminating...
+[2023-09-11 21:12:33,138][88175] Component LearnerWorker_p0 stopped!
+[2023-09-11 21:12:33,139][88175] Waiting for process learner_proc0 to stop...
+[2023-09-11 21:12:33,861][88175] Waiting for process inference_proc0-0 to join...
+[2023-09-11 21:12:33,862][88175] Waiting for process rollout_proc0 to join...
+[2023-09-11 21:12:33,863][88175] Waiting for process rollout_proc1 to join...
+[2023-09-11 21:12:33,864][88175] Waiting for process rollout_proc2 to join...
+[2023-09-11 21:12:33,865][88175] Waiting for process rollout_proc3 to join...
+[2023-09-11 21:12:33,866][88175] Waiting for process rollout_proc4 to join...
+[2023-09-11 21:12:33,867][88175] Waiting for process rollout_proc5 to join...
+[2023-09-11 21:12:33,868][88175] Waiting for process rollout_proc6 to join...
+[2023-09-11 21:12:33,869][88175] Waiting for process rollout_proc7 to join...
+[2023-09-11 21:12:33,870][88175] Batcher 0 profile tree view:
+batching: 0.0252, releasing_batches: 0.0008
+[2023-09-11 21:12:33,871][88175] InferenceWorker_p0-w0 profile tree view:
+wait_policy: 0.0051
+  wait_policy_total: 1.9070
+update_model: 0.4597
+  weight_update: 0.4521
+one_step: 0.0387
+  handle_policy_step: 1.5607
+    deserialize: 0.0316, stack: 0.0038, obs_to_device_normalize: 0.2099, forward: 1.1313, send_messages: 0.0398
+    prepare_outputs: 0.1154
+      to_cpu: 0.0805
+[2023-09-11 21:12:33,872][88175] Learner 0 profile tree view:
+misc: 0.0000, prepare_batch: 2.1990
+train: 3.9226
+  epoch_init: 0.0000, minibatch_init: 0.0000, losses_postprocess: 0.0007, kl_divergence: 0.0012, after_optimizer: 0.0108
+  calculate_losses: 0.3688
+    losses_init: 0.0000, forward_head: 0.3120, bptt_initial: 0.0378, tail: 0.0043, advantages_returns: 0.0011, losses: 0.0092
+    bptt: 0.0039
+      bptt_forward_core: 0.0038
+  update: 3.5401
+    clip: 0.0497
+[2023-09-11 21:12:33,873][88175] RolloutWorker_w0 profile tree view:
+wait_for_trajectories: 0.0007, enqueue_policy_requests: 0.0237, env_step: 0.3045, overhead: 0.0172, complete_rollouts: 0.0006
+save_policy_outputs: 0.0258
+  split_output_tensors: 0.0092
+[2023-09-11 21:12:33,874][88175] RolloutWorker_w7 profile tree view:
+wait_for_trajectories: 0.0006, enqueue_policy_requests: 0.0297, env_step: 0.3797, overhead: 0.0218, complete_rollouts: 0.0007
+save_policy_outputs: 0.0329
+  split_output_tensors: 0.0118
+[2023-09-11 21:12:33,875][88175] Loop Runner_EvtLoop terminating...
+[2023-09-11 21:12:33,876][88175] Runner profile tree view:
+main_loop: 16.2380
+[2023-09-11 21:12:33,876][88175] Collected {0: 4014080}, FPS: 504.5
+[2023-09-11 21:14:09,595][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-11 21:14:09,597][88175] Overriding arg 'num_workers' with value 1 passed from command line
+[2023-09-11 21:14:09,598][88175] Adding new argument 'no_render'=True that is not in the saved config file!
+[2023-09-11 21:14:09,599][88175] Adding new argument 'save_video'=True that is not in the saved config file!
+[2023-09-11 21:14:09,600][88175] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
+[2023-09-11 21:14:09,601][88175] Adding new argument 'video_name'=None that is not in the saved config file!
+[2023-09-11 21:14:09,602][88175] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file!
+[2023-09-11 21:14:09,603][88175] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
+[2023-09-11 21:14:09,605][88175] Adding new argument 'push_to_hub'=False that is not in the saved config file!
+[2023-09-11 21:14:09,605][88175] Adding new argument 'hf_repository'=None that is not in the saved config file!
+[2023-09-11 21:14:09,606][88175] Adding new argument 'policy_index'=0 that is not in the saved config file!
+[2023-09-11 21:14:09,607][88175] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
+[2023-09-11 21:14:09,608][88175] Adding new argument 'train_script'=None that is not in the saved config file!
+[2023-09-11 21:14:09,609][88175] Adding new argument 'enjoy_script'=None that is not in the saved config file!
+[2023-09-11 21:14:09,610][88175] Using frameskip 1 and render_action_repeat=4 for evaluation
+[2023-09-11 21:14:09,640][88175] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:14:09,643][88175] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-11 21:14:09,645][88175] RunningMeanStd input shape: (1,)
+[2023-09-11 21:14:09,668][88175] ConvEncoder: input_channels=3
+[2023-09-11 21:14:09,865][88175] Conv encoder output size: 512
+[2023-09-11 21:14:09,866][88175] Policy head output size: 512
+[2023-09-11 21:14:10,965][88175] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000980_4014080.pth...
+[2023-09-11 21:14:11,814][88175] Num frames 100...
+[2023-09-11 21:14:12,014][88175] Num frames 200...
+[2023-09-11 21:14:12,167][88175] Avg episode rewards: #0: -1.000, true rewards: #0: -1.000
+[2023-09-11 21:14:12,168][88175] Avg episode reward: -1.000, avg true_objective: -1.000
+[2023-09-11 21:14:12,283][88175] Num frames 300...
+[2023-09-11 21:14:12,515][88175] Num frames 400...
+[2023-09-11 21:14:12,764][88175] Num frames 500...
+[2023-09-11 21:14:13,002][88175] Avg episode rewards: #0: -0.500, true rewards: #0: -0.500
+[2023-09-11 21:14:13,004][88175] Avg episode reward: -0.500, avg true_objective: -0.500
+[2023-09-11 21:14:13,043][88175] Num frames 600...
+[2023-09-11 21:14:13,254][88175] Num frames 700...
+[2023-09-11 21:14:13,479][88175] Num frames 800...
+[2023-09-11 21:14:13,606][88175] Avg episode rewards: #0: -0.667, true rewards: #0: -0.667
+[2023-09-11 21:14:13,608][88175] Avg episode reward: -0.667, avg true_objective: -0.667
+[2023-09-11 21:14:13,772][88175] Num frames 900...
+[2023-09-11 21:14:14,008][88175] Num frames 1000...
+[2023-09-11 21:14:14,235][88175] Num frames 1100...
+[2023-09-11 21:14:14,328][88175] Avg episode rewards: #0: -0.750, true rewards: #0: -0.750
+[2023-09-11 21:14:14,329][88175] Avg episode reward: -0.750, avg true_objective: -0.750
+[2023-09-11 21:14:14,583][88175] Num frames 1200...
+[2023-09-11 21:14:14,785][88175] Num frames 1300...
+[2023-09-11 21:14:15,024][88175] Num frames 1400...
+[2023-09-11 21:14:15,198][88175] Avg episode rewards: #0: -0.600, true rewards: #0: -0.600
+[2023-09-11 21:14:15,199][88175] Avg episode reward: -0.600, avg true_objective: -0.600
+[2023-09-11 21:14:15,333][88175] Num frames 1500...
+[2023-09-11 21:14:15,549][88175] Num frames 1600...
+[2023-09-11 21:14:15,840][88175] Num frames 1700...
+[2023-09-11 21:14:16,007][88175] Avg episode rewards: #0: -0.667, true rewards: #0: -0.667
+[2023-09-11 21:14:16,008][88175] Avg episode reward: -0.667, avg true_objective: -0.667
+[2023-09-11 21:14:16,186][88175] Num frames 1800...
+[2023-09-11 21:14:16,427][88175] Num frames 1900...
+[2023-09-11 21:14:16,659][88175] Num frames 2000...
+[2023-09-11 21:14:16,807][88175] Avg episode rewards: #0: -0.714, true rewards: #0: -0.714
+[2023-09-11 21:14:16,809][88175] Avg episode reward: -0.714, avg true_objective: -0.714
+[2023-09-11 21:14:16,932][88175] Num frames 2100...
+[2023-09-11 21:14:17,144][88175] Num frames 2200...
+[2023-09-11 21:14:17,390][88175] Num frames 2300...
+[2023-09-11 21:14:17,653][88175] Avg episode rewards: #0: -0.750, true rewards: #0: -0.750
+[2023-09-11 21:14:17,654][88175] Avg episode reward: -0.750, avg true_objective: -0.750
+[2023-09-11 21:14:17,673][88175] Num frames 2400...
+[2023-09-11 21:14:17,907][88175] Num frames 2500...
+[2023-09-11 21:14:18,141][88175] Num frames 2600...
+[2023-09-11 21:14:18,270][88175] Avg episode rewards: #0: -0.778, true rewards: #0: -0.778
+[2023-09-11 21:14:18,272][88175] Avg episode reward: -0.778, avg true_objective: -0.778
+[2023-09-11 21:14:18,410][88175] Num frames 2700...
+[2023-09-11 21:14:18,690][88175] Num frames 2800...
+[2023-09-11 21:14:18,930][88175] Num frames 2900...
+[2023-09-11 21:14:19,167][88175] Avg episode rewards: #0: -0.800, true rewards: #0: -0.800
+[2023-09-11 21:14:19,168][88175] Avg episode reward: -0.800, avg true_objective: -0.800
+[2023-09-11 21:14:26,808][88175] Replay video saved to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/replay.mp4!
+[2023-09-11 21:15:42,125][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-11 21:15:42,126][88175] Overriding arg 'num_workers' with value 1 passed from command line
+[2023-09-11 21:15:42,127][88175] Adding new argument 'no_render'=True that is not in the saved config file!
+[2023-09-11 21:15:42,127][88175] Adding new argument 'save_video'=True that is not in the saved config file!
+[2023-09-11 21:15:42,128][88175] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
+[2023-09-11 21:15:42,129][88175] Adding new argument 'video_name'=None that is not in the saved config file!
+[2023-09-11 21:15:42,130][88175] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
+[2023-09-11 21:15:42,131][88175] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
+[2023-09-11 21:15:42,132][88175] Adding new argument 'push_to_hub'=True that is not in the saved config file!
+[2023-09-11 21:15:42,133][88175] Adding new argument 'hf_repository'='MattStammers/_vizdoom_defend_the_center' that is not in the saved config file!
+[2023-09-11 21:15:42,133][88175] Adding new argument 'policy_index'=0 that is not in the saved config file!
+[2023-09-11 21:15:42,134][88175] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
+[2023-09-11 21:15:42,135][88175] Adding new argument 'train_script'=None that is not in the saved config file!
+[2023-09-11 21:15:42,136][88175] Adding new argument 'enjoy_script'=None that is not in the saved config file!
+[2023-09-11 21:15:42,137][88175] Using frameskip 1 and render_action_repeat=4 for evaluation
+[2023-09-11 21:15:42,179][88175] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-11 21:15:42,181][88175] RunningMeanStd input shape: (1,)
+[2023-09-11 21:15:42,198][88175] ConvEncoder: input_channels=3
+[2023-09-11 21:15:42,315][88175] Conv encoder output size: 512
+[2023-09-11 21:15:42,316][88175] Policy head output size: 512
+[2023-09-11 21:15:42,342][88175] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000980_4014080.pth...
+[2023-09-11 21:15:43,019][88175] Num frames 100...
+[2023-09-11 21:15:43,211][88175] Num frames 200...
+[2023-09-11 21:15:43,426][88175] Num frames 300...
+[2023-09-11 21:15:43,564][88175] Avg episode rewards: #0: -1.000, true rewards: #0: -1.000
+[2023-09-11 21:15:43,565][88175] Avg episode reward: -1.000, avg true_objective: -1.000
+[2023-09-11 21:15:43,705][88175] Num frames 400...
+[2023-09-11 21:15:44,003][88175] Num frames 500...
+[2023-09-11 21:15:44,229][88175] Num frames 600...
+[2023-09-11 21:15:44,471][88175] Avg episode rewards: #0: -0.500, true rewards: #0: -0.500
+[2023-09-11 21:15:44,478][88175] Avg episode reward: -0.500, avg true_objective: -0.500
+[2023-09-11 21:15:44,541][88175] Num frames 700...
+[2023-09-11 21:15:44,786][88175] Num frames 800...
+[2023-09-11 21:15:45,058][88175] Num frames 900...
+[2023-09-11 21:15:45,156][88175] Avg episode rewards: #0: -0.667, true rewards: #0: -0.667
+[2023-09-11 21:15:45,158][88175] Avg episode reward: -0.667, avg true_objective: -0.667
+[2023-09-11 21:15:45,353][88175] Num frames 1000...
+[2023-09-11 21:15:45,556][88175] Num frames 1100...
+[2023-09-11 21:15:45,781][88175] Num frames 1200...
+[2023-09-11 21:15:45,966][88175] Avg episode rewards: #0: -0.750, true rewards: #0: -0.750
+[2023-09-11 21:15:45,968][88175] Avg episode reward: -0.750, avg true_objective: -0.750
+[2023-09-11 21:15:46,051][88175] Num frames 1300...
+[2023-09-11 21:15:46,264][88175] Num frames 1400...
+[2023-09-11 21:15:46,481][88175] Num frames 1500...
+[2023-09-11 21:15:46,675][88175] Avg episode rewards: #0: -0.800, true rewards: #0: -0.800
+[2023-09-11 21:15:46,677][88175] Avg episode reward: -0.800, avg true_objective: -0.800
+[2023-09-11 21:15:46,784][88175] Num frames 1600...
+[2023-09-11 21:15:47,026][88175] Num frames 1700...
+[2023-09-11 21:15:47,241][88175] Num frames 1800...
+[2023-09-11 21:15:47,399][88175] Avg episode rewards: #0: -0.833, true rewards: #0: -0.833
+[2023-09-11 21:15:47,400][88175] Avg episode reward: -0.833, avg true_objective: -0.833
+[2023-09-11 21:15:47,521][88175] Num frames 1900...
+[2023-09-11 21:15:47,753][88175] Num frames 2000...
+[2023-09-11 21:15:47,985][88175] Num frames 2100...
+[2023-09-11 21:15:48,115][88175] Avg episode rewards: #0: -0.857, true rewards: #0: -0.857
+[2023-09-11 21:15:48,116][88175] Avg episode reward: -0.857, avg true_objective: -0.857
+[2023-09-11 21:15:48,292][88175] Num frames 2200...
+[2023-09-11 21:15:48,534][88175] Num frames 2300...
+[2023-09-11 21:15:48,757][88175] Num frames 2400...
+[2023-09-11 21:15:48,888][88175] Avg episode rewards: #0: -0.875, true rewards: #0: -0.875
+[2023-09-11 21:15:48,890][88175] Avg episode reward: -0.875, avg true_objective: -0.875
+[2023-09-11 21:15:49,063][88175] Num frames 2500...
+[2023-09-11 21:15:49,275][88175] Num frames 2600...
+[2023-09-11 21:15:49,550][88175] Num frames 2700...
+[2023-09-11 21:15:49,789][88175] Avg episode rewards: #0: -0.778, true rewards: #0: -0.778
+[2023-09-11 21:15:49,791][88175] Avg episode reward: -0.778, avg true_objective: -0.778
+[2023-09-11 21:15:49,846][88175] Num frames 2800...
+[2023-09-11 21:15:50,111][88175] Num frames 2900...
+[2023-09-11 21:15:50,363][88175] Num frames 3000...
+[2023-09-11 21:15:50,437][88175] Avg episode rewards: #0: -0.800, true rewards: #0: -0.800
+[2023-09-11 21:15:50,439][88175] Avg episode reward: -0.800, avg true_objective: -0.800
+[2023-09-11 21:15:57,637][88175] Replay video saved to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/replay.mp4!
+[2023-09-11 21:17:01,060][88175] The model has been pushed to https://huggingface.co/MattStammers/_vizdoom_defend_the_center
+[2023-09-11 21:20:06,167][88175] Environment doom_basic already registered, overwriting...
+[2023-09-11 21:20:06,176][88175] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-11 21:20:06,183][88175] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-11 21:20:06,191][88175] Environment doom_dm already registered, overwriting...
+[2023-09-11 21:20:06,199][88175] Environment doom_dwango5 already registered, overwriting...
+[2023-09-11 21:20:06,207][88175] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-11 21:20:06,215][88175] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-11 21:20:06,222][88175] Environment doom_my_way_home already registered, overwriting...
+[2023-09-11 21:20:06,227][88175] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-11 21:20:06,232][88175] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-11 21:20:06,239][88175] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-11 21:20:06,242][88175] Environment doom_health_gathering already registered, overwriting...
+[2023-09-11 21:20:06,246][88175] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-11 21:20:06,249][88175] Environment doom_battle already registered, overwriting...
+[2023-09-11 21:20:06,252][88175] Environment doom_battle2 already registered, overwriting...
+[2023-09-11 21:20:06,259][88175] Environment doom_duel_bots already registered, overwriting...
+[2023-09-11 21:20:06,261][88175] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-11 21:20:06,263][88175] Environment doom_duel already registered, overwriting...
+[2023-09-11 21:20:06,267][88175] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-11 21:20:06,275][88175] Environment doom_benchmark already registered, overwriting...
+[2023-09-11 21:20:06,279][88175] register_encoder_factory: <function make_vizdoom_encoder at 0x7fdf25a3f5b0>
+[2023-09-11 21:20:06,332][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-11 21:20:06,339][88175] Overriding arg 'train_for_env_steps' with value 40000000 passed from command line
+[2023-09-11 21:20:06,349][88175] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-11 21:20:06,355][88175] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-11 21:20:06,363][88175] Weights and Biases integration disabled
+[2023-09-11 21:20:06,383][88175] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-11 21:20:10,678][88175] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_defend_the_center
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=40000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-11 21:20:10,687][88175] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-11 21:20:11,707][88175] Rollout worker 0 uses device cpu
+[2023-09-11 21:20:11,710][88175] Rollout worker 1 uses device cpu
+[2023-09-11 21:20:11,714][88175] Rollout worker 2 uses device cpu
+[2023-09-11 21:20:11,715][88175] Rollout worker 3 uses device cpu
+[2023-09-11 21:20:11,717][88175] Rollout worker 4 uses device cpu
+[2023-09-11 21:20:11,721][88175] Rollout worker 5 uses device cpu
+[2023-09-11 21:20:11,723][88175] Rollout worker 6 uses device cpu
+[2023-09-11 21:20:11,724][88175] Rollout worker 7 uses device cpu
+[2023-09-11 21:20:12,027][88175] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:20:12,028][88175] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-11 21:20:12,070][88175] Starting all processes...
+[2023-09-11 21:20:12,073][88175] Starting process learner_proc0
+[2023-09-11 21:20:12,142][88175] Starting all processes...
+[2023-09-11 21:20:12,212][88175] Starting process inference_proc0-0
+[2023-09-11 21:20:12,216][88175] Starting process rollout_proc0
+[2023-09-11 21:20:12,235][88175] Starting process rollout_proc1
+[2023-09-11 21:20:12,237][88175] Starting process rollout_proc2
+[2023-09-11 21:20:12,263][88175] Starting process rollout_proc3
+[2023-09-11 21:20:12,263][88175] Starting process rollout_proc4
+[2023-09-11 21:20:12,263][88175] Starting process rollout_proc5
+[2023-09-11 21:20:12,263][88175] Starting process rollout_proc6
+[2023-09-11 21:20:12,275][88175] Starting process rollout_proc7
+[2023-09-11 21:20:15,260][83015] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-11 21:20:15,278][83016] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-11 21:20:15,372][82980] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:20:15,373][82980] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-11 21:20:15,399][82980] Num visible devices: 1
+[2023-09-11 21:20:15,511][82980] Starting seed is not provided
+[2023-09-11 21:20:15,511][82980] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:20:15,511][82980] Initializing actor-critic model on device cuda:0
+[2023-09-11 21:20:15,512][82980] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-11 21:20:15,513][82980] RunningMeanStd input shape: (1,)
+[2023-09-11 21:20:15,519][83013] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:20:15,519][83013] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-11 21:20:15,537][82980] ConvEncoder: input_channels=3
+[2023-09-11 21:20:15,552][83013] Num visible devices: 1
+[2023-09-11 21:20:15,570][83070] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-11 21:20:15,588][83068] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-11 21:20:15,615][83014] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-11 21:20:15,619][83065] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-11 21:20:15,671][83067] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-11 21:20:15,718][83069] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-11 21:20:15,794][82980] Conv encoder output size: 512
+[2023-09-11 21:20:15,796][82980] Policy head output size: 512
+[2023-09-11 21:20:15,816][82980] Created Actor Critic model with architecture:
+[2023-09-11 21:20:15,817][82980] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(512, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=5, bias=True)
+  )
+)
+[2023-09-11 21:20:17,198][82980] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-11 21:20:17,201][82980] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000980_4014080.pth...
+[2023-09-11 21:20:17,263][82980] Loading model from checkpoint
+[2023-09-11 21:20:17,269][82980] Loaded experiment state at self.train_step=980, self.env_steps=4014080
+[2023-09-11 21:20:17,270][82980] Initialized policy 0 weights for model version 980
+[2023-09-11 21:20:17,273][82980] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-11 21:20:17,273][82980] LearnerWorker_p0 finished initialization!
+[2023-09-11 21:20:18,170][83013] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-11 21:20:18,171][83013] RunningMeanStd input shape: (1,)
+[2023-09-11 21:20:18,192][83013] ConvEncoder: input_channels=3
+[2023-09-11 21:20:18,372][83013] Conv encoder output size: 512
+[2023-09-11 21:20:18,373][83013] Policy head output size: 512
+[2023-09-11 21:20:18,880][88175] Inference worker 0-0 is ready!
+[2023-09-11 21:20:18,881][88175] All inference workers are ready! Signal rollout workers to start!
+[2023-09-11 21:20:18,929][83067] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:20:18,935][83070] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:20:18,944][83014] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:20:18,951][83065] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:20:18,960][83068] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:20:18,961][83015] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:20:19,005][83069] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:20:19,032][83016] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-11 21:20:19,499][83068] Decorrelating experience for 0 frames...
+[2023-09-11 21:20:19,513][83069] Decorrelating experience for 0 frames...
+[2023-09-11 21:20:19,543][83065] Decorrelating experience for 0 frames...
+[2023-09-11 21:20:19,548][83014] Decorrelating experience for 0 frames...
+[2023-09-11 21:20:19,563][83067] Decorrelating experience for 0 frames...
+[2023-09-11 21:20:19,605][83016] Decorrelating experience for 0 frames...
+[2023-09-11 21:20:19,606][83015] Decorrelating experience for 0 frames...
+[2023-09-11 21:20:19,753][83070] Decorrelating experience for 0 frames...
+[2023-09-11 21:20:20,045][83014] Decorrelating experience for 32 frames...
+[2023-09-11 21:20:20,069][83068] Decorrelating experience for 32 frames...
+[2023-09-11 21:20:20,116][83065] Decorrelating experience for 32 frames...
+[2023-09-11 21:20:20,116][83016] Decorrelating experience for 32 frames...
+[2023-09-11 21:20:20,243][83067] Decorrelating experience for 32 frames...
+[2023-09-11 21:20:20,580][83015] Decorrelating experience for 32 frames...
+[2023-09-11 21:20:20,642][83068] Decorrelating experience for 64 frames...
+[2023-09-11 21:20:20,743][83070] Decorrelating experience for 32 frames...
+[2023-09-11 21:20:20,988][83067] Decorrelating experience for 64 frames...
+[2023-09-11 21:20:21,210][83016] Decorrelating experience for 64 frames...
+[2023-09-11 21:20:21,307][83015] Decorrelating experience for 64 frames...
+[2023-09-11 21:20:21,320][83068] Decorrelating experience for 96 frames...
+[2023-09-11 21:20:21,351][83065] Decorrelating experience for 64 frames...
+[2023-09-11 21:20:21,372][83069] Decorrelating experience for 32 frames...
+[2023-09-11 21:20:21,386][88175] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 4014080. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
+[2023-09-11 21:20:21,615][83067] Decorrelating experience for 96 frames...
+[2023-09-11 21:20:21,646][83070] Decorrelating experience for 64 frames...
+[2023-09-11 21:20:21,859][83015] Decorrelating experience for 96 frames...
+[2023-09-11 21:20:21,868][83014] Decorrelating experience for 64 frames...
+[2023-09-11 21:20:21,891][83016] Decorrelating experience for 96 frames...
+[2023-09-11 21:20:22,178][83065] Decorrelating experience for 96 frames...
+[2023-09-11 21:20:22,331][83069] Decorrelating experience for 64 frames...
+[2023-09-11 21:20:22,347][83014] Decorrelating experience for 96 frames...
+[2023-09-11 21:20:22,663][83070] Decorrelating experience for 96 frames...
+[2023-09-11 21:20:22,810][83069] Decorrelating experience for 96 frames...
+[2023-09-11 21:20:23,792][82980] Signal inference workers to stop experience collection...
+[2023-09-11 21:20:23,807][83013] InferenceWorker_p0-w0: stopping experience collection
+[2023-09-11 21:20:26,384][88175] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 4014080. Throughput: 0: 515.0. Samples: 2574. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
+[2023-09-11 21:20:26,388][88175] Avg episode reward: [(0, '-0.833')]
+[2023-09-11 21:20:27,794][82980] Signal inference workers to resume experience collection...
+[2023-09-11 21:20:27,795][83013] InferenceWorker_p0-w0: resuming experience collection
+[2023-09-11 21:20:31,387][88175] Fps is (10 sec: 2457.3, 60 sec: 2457.3, 300 sec: 2457.3). Total num frames: 4038656. Throughput: 0: 652.5. Samples: 6526. Policy #0 lag: (min: 0.0, avg: 1.0, max: 3.0)
+[2023-09-11 21:20:31,397][88175] Avg episode reward: [(0, '-0.702')]
+[2023-09-11 21:20:32,019][88175] Heartbeat connected on Batcher_0
+[2023-09-11 21:20:32,023][88175] Heartbeat connected on LearnerWorker_p0
+[2023-09-11 21:20:32,041][88175] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-11 21:20:32,043][88175] Heartbeat connected on RolloutWorker_w0
+[2023-09-11 21:20:32,049][88175] Heartbeat connected on RolloutWorker_w2
+[2023-09-11 21:20:32,051][88175] Heartbeat connected on RolloutWorker_w1
+[2023-09-11 21:20:32,055][88175] Heartbeat connected on RolloutWorker_w3
+[2023-09-11 21:20:32,066][88175] Heartbeat connected on RolloutWorker_w6
+[2023-09-11 21:20:32,071][88175] Heartbeat connected on RolloutWorker_w4
+[2023-09-11 21:20:32,080][88175] Heartbeat connected on RolloutWorker_w5
+[2023-09-11 21:20:32,090][88175] Heartbeat connected on RolloutWorker_w7
+[2023-09-11 21:20:33,437][83013] Updated weights for policy 0, policy_version 990 (0.0011)
+[2023-09-11 21:20:36,383][88175] Fps is (10 sec: 6144.3, 60 sec: 4096.6, 300 sec: 4096.6). Total num frames: 4075520. Throughput: 0: 783.2. Samples: 11746. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:20:36,385][88175] Avg episode reward: [(0, '0.540')]
+[2023-09-11 21:20:39,020][83013] Updated weights for policy 0, policy_version 1000 (0.0012)
+[2023-09-11 21:20:41,383][88175] Fps is (10 sec: 7375.4, 60 sec: 4915.7, 300 sec: 4915.7). Total num frames: 4112384. Throughput: 0: 1131.1. Samples: 22620. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:20:41,387][88175] Avg episode reward: [(0, '2.910')]
+[2023-09-11 21:20:43,729][83013] Updated weights for policy 0, policy_version 1010 (0.0013)
+[2023-09-11 21:20:46,383][88175] Fps is (10 sec: 8191.9, 60 sec: 5734.9, 300 sec: 5734.9). Total num frames: 4157440. Throughput: 0: 1423.4. Samples: 35582. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:20:46,385][88175] Avg episode reward: [(0, '3.860')]
+[2023-09-11 21:20:48,830][83013] Updated weights for policy 0, policy_version 1020 (0.0018)
+[2023-09-11 21:20:51,383][88175] Fps is (10 sec: 8192.0, 60 sec: 6007.9, 300 sec: 6007.9). Total num frames: 4194304. Throughput: 0: 1388.6. Samples: 41656. Policy #0 lag: (min: 0.0, avg: 0.8, max: 1.0)
+[2023-09-11 21:20:51,386][88175] Avg episode reward: [(0, '3.940')]
+[2023-09-11 21:20:54,170][83013] Updated weights for policy 0, policy_version 1030 (0.0014)
+[2023-09-11 21:20:56,383][88175] Fps is (10 sec: 7782.4, 60 sec: 6319.9, 300 sec: 6319.9). Total num frames: 4235264. Throughput: 0: 1535.0. Samples: 53720. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:20:56,385][88175] Avg episode reward: [(0, '3.840')]
+[2023-09-11 21:20:58,954][83013] Updated weights for policy 0, policy_version 1040 (0.0011)
+[2023-09-11 21:21:01,383][88175] Fps is (10 sec: 8601.6, 60 sec: 6656.4, 300 sec: 6656.4). Total num frames: 4280320. Throughput: 0: 1674.2. Samples: 66964. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:21:01,386][88175] Avg episode reward: [(0, '3.680')]
+[2023-09-11 21:21:03,727][83013] Updated weights for policy 0, policy_version 1050 (0.0011)
+[2023-09-11 21:21:06,383][88175] Fps is (10 sec: 8192.1, 60 sec: 6736.0, 300 sec: 6736.0). Total num frames: 4317184. Throughput: 0: 1615.5. Samples: 72692. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:21:06,385][88175] Avg episode reward: [(0, '3.530')]
+[2023-09-11 21:21:09,243][83013] Updated weights for policy 0, policy_version 1060 (0.0016)
+[2023-09-11 21:21:11,383][88175] Fps is (10 sec: 7372.8, 60 sec: 6799.7, 300 sec: 6799.7). Total num frames: 4354048. Throughput: 0: 1799.8. Samples: 83564. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:21:11,385][88175] Avg episode reward: [(0, '3.260')]
+[2023-09-11 21:21:14,348][83013] Updated weights for policy 0, policy_version 1070 (0.0013)
+[2023-09-11 21:21:16,384][88175] Fps is (10 sec: 7782.1, 60 sec: 6926.2, 300 sec: 6926.2). Total num frames: 4395008. Throughput: 0: 1985.3. Samples: 95858. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:21:16,385][88175] Avg episode reward: [(0, '3.310')]
+[2023-09-11 21:21:18,853][83013] Updated weights for policy 0, policy_version 1080 (0.0011)
+[2023-09-11 21:21:21,383][88175] Fps is (10 sec: 9420.7, 60 sec: 7236.5, 300 sec: 7236.5). Total num frames: 4448256. Throughput: 0: 2033.1. Samples: 103234. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:21:21,385][88175] Avg episode reward: [(0, '3.370')]
+[2023-09-11 21:21:22,883][83013] Updated weights for policy 0, policy_version 1090 (0.0012)
+[2023-09-11 21:21:26,383][88175] Fps is (10 sec: 10650.0, 60 sec: 8123.8, 300 sec: 7499.1). Total num frames: 4501504. Throughput: 0: 2146.3. Samples: 119202. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:21:26,385][88175] Avg episode reward: [(0, '3.250')]
+[2023-09-11 21:21:26,404][83013] Updated weights for policy 0, policy_version 1100 (0.0011)
+[2023-09-11 21:21:29,903][83013] Updated weights for policy 0, policy_version 1110 (0.0010)
+[2023-09-11 21:21:31,383][88175] Fps is (10 sec: 11878.5, 60 sec: 8806.9, 300 sec: 7899.7). Total num frames: 4567040. Throughput: 0: 2268.9. Samples: 137682. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:21:31,384][88175] Avg episode reward: [(0, '3.490')]
+[2023-09-11 21:21:32,776][83013] Updated weights for policy 0, policy_version 1120 (0.0012)
+[2023-09-11 21:21:35,566][83013] Updated weights for policy 0, policy_version 1130 (0.0010)
+[2023-09-11 21:21:36,383][88175] Fps is (10 sec: 13926.3, 60 sec: 9420.8, 300 sec: 8356.1). Total num frames: 4640768. Throughput: 0: 2368.0. Samples: 148218. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:21:36,385][88175] Avg episode reward: [(0, '3.450')]
+[2023-09-11 21:21:38,573][83013] Updated weights for policy 0, policy_version 1140 (0.0010)
+[2023-09-11 21:21:41,383][88175] Fps is (10 sec: 13926.4, 60 sec: 9898.7, 300 sec: 8653.0). Total num frames: 4706304. Throughput: 0: 2576.7. Samples: 169672. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:21:41,385][88175] Avg episode reward: [(0, '3.160')]
+[2023-09-11 21:21:41,474][83013] Updated weights for policy 0, policy_version 1150 (0.0011)
+[2023-09-11 21:21:45,242][83013] Updated weights for policy 0, policy_version 1160 (0.0011)
+[2023-09-11 21:21:46,383][88175] Fps is (10 sec: 12288.1, 60 sec: 10103.5, 300 sec: 8818.7). Total num frames: 4763648. Throughput: 0: 2671.3. Samples: 187174. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:21:46,385][88175] Avg episode reward: [(0, '3.350')]
+[2023-09-11 21:21:48,592][83013] Updated weights for policy 0, policy_version 1170 (0.0011)
+[2023-09-11 21:21:51,383][88175] Fps is (10 sec: 11878.3, 60 sec: 10513.1, 300 sec: 9011.4). Total num frames: 4825088. Throughput: 0: 2746.2. Samples: 196272. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:21:51,385][88175] Avg episode reward: [(0, '3.390')]
+[2023-09-11 21:21:51,910][83013] Updated weights for policy 0, policy_version 1180 (0.0011)
+[2023-09-11 21:21:55,040][83013] Updated weights for policy 0, policy_version 1190 (0.0011)
+[2023-09-11 21:21:56,383][88175] Fps is (10 sec: 12697.6, 60 sec: 10922.7, 300 sec: 9227.0). Total num frames: 4890624. Throughput: 0: 2926.1. Samples: 215240. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:21:56,384][88175] Avg episode reward: [(0, '3.340')]
+[2023-09-11 21:21:57,933][83013] Updated weights for policy 0, policy_version 1200 (0.0011)
+[2023-09-11 21:22:00,647][83013] Updated weights for policy 0, policy_version 1210 (0.0010)
+[2023-09-11 21:22:01,383][88175] Fps is (10 sec: 13926.4, 60 sec: 11400.5, 300 sec: 9502.9). Total num frames: 4964352. Throughput: 0: 3139.4. Samples: 237130. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:22:01,385][88175] Avg episode reward: [(0, '3.260')]
+[2023-09-11 21:22:03,475][83013] Updated weights for policy 0, policy_version 1220 (0.0010)
+[2023-09-11 21:22:06,213][83013] Updated weights for policy 0, policy_version 1230 (0.0010)
+[2023-09-11 21:22:06,383][88175] Fps is (10 sec: 14745.5, 60 sec: 12014.9, 300 sec: 9752.6). Total num frames: 5038080. Throughput: 0: 3217.7. Samples: 248030. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:22:06,385][88175] Avg episode reward: [(0, '3.220')]
+[2023-09-11 21:22:06,452][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001231_5042176.pth...
+[2023-09-11 21:22:06,503][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth
+[2023-09-11 21:22:08,899][83013] Updated weights for policy 0, policy_version 1240 (0.0010)
+[2023-09-11 21:22:11,383][88175] Fps is (10 sec: 15155.3, 60 sec: 12697.6, 300 sec: 10016.8). Total num frames: 5115904. Throughput: 0: 3364.6. Samples: 270608. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:22:11,385][88175] Avg episode reward: [(0, '3.240')]
+[2023-09-11 21:22:11,613][83013] Updated weights for policy 0, policy_version 1250 (0.0009)
+[2023-09-11 21:22:14,305][83013] Updated weights for policy 0, policy_version 1260 (0.0009)
+[2023-09-11 21:22:16,383][88175] Fps is (10 sec: 15155.3, 60 sec: 13243.8, 300 sec: 10222.4). Total num frames: 5189632. Throughput: 0: 3447.0. Samples: 292798. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:22:16,385][88175] Avg episode reward: [(0, '3.280')]
+[2023-09-11 21:22:17,178][83013] Updated weights for policy 0, policy_version 1270 (0.0010)
+[2023-09-11 21:22:20,749][83013] Updated weights for policy 0, policy_version 1280 (0.0011)
+[2023-09-11 21:22:21,383][88175] Fps is (10 sec: 13107.1, 60 sec: 13312.0, 300 sec: 10274.3). Total num frames: 5246976. Throughput: 0: 3432.1. Samples: 302664. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:22:21,385][88175] Avg episode reward: [(0, '3.500')]
+[2023-09-11 21:22:24,332][83013] Updated weights for policy 0, policy_version 1290 (0.0010)
+[2023-09-11 21:22:26,383][88175] Fps is (10 sec: 11468.8, 60 sec: 13380.3, 300 sec: 10322.1). Total num frames: 5304320. Throughput: 0: 3335.1. Samples: 319750. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:22:26,385][88175] Avg episode reward: [(0, '3.180')]
+[2023-09-11 21:22:27,894][83013] Updated weights for policy 0, policy_version 1300 (0.0010)
+[2023-09-11 21:22:31,383][88175] Fps is (10 sec: 11468.9, 60 sec: 13243.7, 300 sec: 10366.2). Total num frames: 5361664. Throughput: 0: 3333.4. Samples: 337176. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:22:31,385][88175] Avg episode reward: [(0, '3.290')]
+[2023-09-11 21:22:31,418][83013] Updated weights for policy 0, policy_version 1310 (0.0009)
+[2023-09-11 21:22:34,930][83013] Updated weights for policy 0, policy_version 1320 (0.0009)
+[2023-09-11 21:22:36,383][88175] Fps is (10 sec: 11878.4, 60 sec: 13039.0, 300 sec: 10437.4). Total num frames: 5423104. Throughput: 0: 3325.8. Samples: 345934. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:22:36,384][88175] Avg episode reward: [(0, '3.300')]
+[2023-09-11 21:22:38,361][83013] Updated weights for policy 0, policy_version 1330 (0.0009)
+[2023-09-11 21:22:41,383][88175] Fps is (10 sec: 11878.4, 60 sec: 12902.4, 300 sec: 10474.2). Total num frames: 5480448. Throughput: 0: 3296.4. Samples: 363580. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:22:41,384][88175] Avg episode reward: [(0, '3.470')]
+[2023-09-11 21:22:41,911][83013] Updated weights for policy 0, policy_version 1340 (0.0008)
+[2023-09-11 21:22:45,330][83013] Updated weights for policy 0, policy_version 1350 (0.0009)
+[2023-09-11 21:22:46,384][88175] Fps is (10 sec: 11877.8, 60 sec: 12970.6, 300 sec: 10536.7). Total num frames: 5541888. Throughput: 0: 3202.5. Samples: 381246. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:22:46,386][88175] Avg episode reward: [(0, '3.540')]
+[2023-09-11 21:22:48,846][83013] Updated weights for policy 0, policy_version 1360 (0.0008)
+[2023-09-11 21:22:51,383][88175] Fps is (10 sec: 11878.4, 60 sec: 12902.4, 300 sec: 10567.8). Total num frames: 5599232. Throughput: 0: 3149.4. Samples: 389754. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:22:51,385][88175] Avg episode reward: [(0, '3.360')]
+[2023-09-11 21:22:52,370][83013] Updated weights for policy 0, policy_version 1370 (0.0008)
+[2023-09-11 21:22:55,957][83013] Updated weights for policy 0, policy_version 1380 (0.0009)
+[2023-09-11 21:22:56,384][88175] Fps is (10 sec: 11469.2, 60 sec: 12765.8, 300 sec: 10596.9). Total num frames: 5656576. Throughput: 0: 3036.8. Samples: 407264. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:22:56,385][88175] Avg episode reward: [(0, '3.370')]
+[2023-09-11 21:22:59,597][83013] Updated weights for policy 0, policy_version 1390 (0.0008)
+[2023-09-11 21:23:01,384][88175] Fps is (10 sec: 11468.7, 60 sec: 12492.8, 300 sec: 10624.1). Total num frames: 5713920. Throughput: 0: 2925.4. Samples: 424442. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:23:01,385][88175] Avg episode reward: [(0, '3.410')]
+[2023-09-11 21:23:03,142][83013] Updated weights for policy 0, policy_version 1400 (0.0008)
+[2023-09-11 21:23:06,383][88175] Fps is (10 sec: 9011.3, 60 sec: 11810.1, 300 sec: 10500.8). Total num frames: 5746688. Throughput: 0: 2859.9. Samples: 431360. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:23:06,385][88175] Avg episode reward: [(0, '3.390')]
+[2023-09-11 21:23:08,583][83013] Updated weights for policy 0, policy_version 1410 (0.0009)
+[2023-09-11 21:23:11,383][88175] Fps is (10 sec: 9420.9, 60 sec: 11537.1, 300 sec: 10553.4). Total num frames: 5808128. Throughput: 0: 2781.3. Samples: 444908. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:23:11,385][88175] Avg episode reward: [(0, '3.350')]
+[2023-09-11 21:23:12,070][83013] Updated weights for policy 0, policy_version 1420 (0.0008)
+[2023-09-11 21:23:15,653][83013] Updated weights for policy 0, policy_version 1430 (0.0008)
+[2023-09-11 21:23:16,384][88175] Fps is (10 sec: 11878.1, 60 sec: 11264.0, 300 sec: 10579.5). Total num frames: 5865472. Throughput: 0: 2781.3. Samples: 462336. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:23:16,385][88175] Avg episode reward: [(0, '3.310')]
+[2023-09-11 21:23:19,211][83013] Updated weights for policy 0, policy_version 1440 (0.0008)
+[2023-09-11 21:23:21,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11264.0, 300 sec: 10604.2). Total num frames: 5922816. Throughput: 0: 2775.5. Samples: 470834. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:23:21,385][88175] Avg episode reward: [(0, '3.160')]
+[2023-09-11 21:23:22,720][83013] Updated weights for policy 0, policy_version 1450 (0.0008)
+[2023-09-11 21:23:26,251][83013] Updated weights for policy 0, policy_version 1460 (0.0008)
+[2023-09-11 21:23:26,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11263.9, 300 sec: 10627.6). Total num frames: 5980160. Throughput: 0: 2771.8. Samples: 488314. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:23:26,385][88175] Avg episode reward: [(0, '3.130')]
+[2023-09-11 21:23:29,860][83013] Updated weights for policy 0, policy_version 1470 (0.0009)
+[2023-09-11 21:23:31,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11264.0, 300 sec: 10649.7). Total num frames: 6037504. Throughput: 0: 2761.6. Samples: 505518. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:23:31,385][88175] Avg episode reward: [(0, '3.320')]
+[2023-09-11 21:23:33,393][83013] Updated weights for policy 0, policy_version 1480 (0.0008)
+[2023-09-11 21:23:36,383][88175] Fps is (10 sec: 11469.2, 60 sec: 11195.7, 300 sec: 10670.7). Total num frames: 6094848. Throughput: 0: 2762.5. Samples: 514066. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:23:36,385][88175] Avg episode reward: [(0, '3.270')]
+[2023-09-11 21:23:37,027][83013] Updated weights for policy 0, policy_version 1490 (0.0009)
+[2023-09-11 21:23:40,551][83013] Updated weights for policy 0, policy_version 1500 (0.0009)
+[2023-09-11 21:23:41,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11195.7, 300 sec: 10690.7). Total num frames: 6152192. Throughput: 0: 2758.4. Samples: 531390. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:23:41,385][88175] Avg episode reward: [(0, '3.160')]
+[2023-09-11 21:23:44,070][83013] Updated weights for policy 0, policy_version 1510 (0.0008)
+[2023-09-11 21:23:46,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11127.6, 300 sec: 10709.7). Total num frames: 6209536. Throughput: 0: 2756.2. Samples: 548472. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:23:46,385][88175] Avg episode reward: [(0, '3.270')]
+[2023-09-11 21:23:47,696][83013] Updated weights for policy 0, policy_version 1520 (0.0009)
+[2023-09-11 21:23:51,252][83013] Updated weights for policy 0, policy_version 1530 (0.0008)
+[2023-09-11 21:23:51,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11127.5, 300 sec: 10727.7). Total num frames: 6266880. Throughput: 0: 2796.6. Samples: 557206. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:23:51,384][88175] Avg episode reward: [(0, '3.430')]
+[2023-09-11 21:23:54,907][83013] Updated weights for policy 0, policy_version 1540 (0.0008)
+[2023-09-11 21:23:56,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11127.5, 300 sec: 10745.0). Total num frames: 6324224. Throughput: 0: 2872.2. Samples: 574156. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:23:56,385][88175] Avg episode reward: [(0, '3.260')]
+[2023-09-11 21:23:58,495][83013] Updated weights for policy 0, policy_version 1550 (0.0008)
+[2023-09-11 21:24:01,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11127.5, 300 sec: 10761.4). Total num frames: 6381568. Throughput: 0: 2871.7. Samples: 591562. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:24:01,385][88175] Avg episode reward: [(0, '3.250')]
+[2023-09-11 21:24:02,005][83013] Updated weights for policy 0, policy_version 1560 (0.0009)
+[2023-09-11 21:24:05,557][83013] Updated weights for policy 0, policy_version 1570 (0.0009)
+[2023-09-11 21:24:06,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 10777.1). Total num frames: 6438912. Throughput: 0: 2870.9. Samples: 600026. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:24:06,384][88175] Avg episode reward: [(0, '3.210')]
+[2023-09-11 21:24:06,390][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001572_6438912.pth...
+[2023-09-11 21:24:06,443][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000980_4014080.pth
+[2023-09-11 21:24:09,106][83013] Updated weights for policy 0, policy_version 1580 (0.0009)
+[2023-09-11 21:24:11,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 10792.2). Total num frames: 6496256. Throughput: 0: 2865.7. Samples: 617270. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:24:11,385][88175] Avg episode reward: [(0, '3.500')]
+[2023-09-11 21:24:12,638][83013] Updated weights for policy 0, policy_version 1590 (0.0008)
+[2023-09-11 21:24:16,244][83013] Updated weights for policy 0, policy_version 1600 (0.0008)
+[2023-09-11 21:24:16,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 10806.6). Total num frames: 6553600. Throughput: 0: 2869.6. Samples: 634650. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:24:16,385][88175] Avg episode reward: [(0, '3.150')]
+[2023-09-11 21:24:19,740][83013] Updated weights for policy 0, policy_version 1610 (0.0009)
+[2023-09-11 21:24:21,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 10820.4). Total num frames: 6610944. Throughput: 0: 2872.5. Samples: 643330. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:24:21,384][88175] Avg episode reward: [(0, '3.380')]
+[2023-09-11 21:24:23,364][83013] Updated weights for policy 0, policy_version 1620 (0.0008)
+[2023-09-11 21:24:26,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 10833.6). Total num frames: 6668288. Throughput: 0: 2866.4. Samples: 660380. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:24:26,385][88175] Avg episode reward: [(0, '3.140')]
+[2023-09-11 21:24:26,980][83013] Updated weights for policy 0, policy_version 1630 (0.0008)
+[2023-09-11 21:24:30,501][83013] Updated weights for policy 0, policy_version 1640 (0.0009)
+[2023-09-11 21:24:31,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 10846.3). Total num frames: 6725632. Throughput: 0: 2868.4. Samples: 677550. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:24:31,385][88175] Avg episode reward: [(0, '3.070')]
+[2023-09-11 21:24:34,084][83013] Updated weights for policy 0, policy_version 1650 (0.0008)
+[2023-09-11 21:24:36,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 10858.5). Total num frames: 6782976. Throughput: 0: 2867.9. Samples: 686262. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:24:36,384][88175] Avg episode reward: [(0, '3.030')]
+[2023-09-11 21:24:37,562][83013] Updated weights for policy 0, policy_version 1660 (0.0009)
+[2023-09-11 21:24:41,127][83013] Updated weights for policy 0, policy_version 1670 (0.0009)
+[2023-09-11 21:24:41,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 10870.2). Total num frames: 6840320. Throughput: 0: 2878.4. Samples: 703682. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:24:41,385][88175] Avg episode reward: [(0, '3.200')]
+[2023-09-11 21:24:44,786][83013] Updated weights for policy 0, policy_version 1680 (0.0009)
+[2023-09-11 21:24:46,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 10881.5). Total num frames: 6897664. Throughput: 0: 2872.6. Samples: 720830. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:24:46,385][88175] Avg episode reward: [(0, '3.430')]
+[2023-09-11 21:24:48,372][83013] Updated weights for policy 0, policy_version 1690 (0.0008)
+[2023-09-11 21:24:51,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11468.8, 300 sec: 10892.4). Total num frames: 6955008. Throughput: 0: 2878.9. Samples: 729578. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:24:51,386][88175] Avg episode reward: [(0, '3.240')]
+[2023-09-11 21:24:51,919][83013] Updated weights for policy 0, policy_version 1700 (0.0008)
+[2023-09-11 21:24:55,435][83013] Updated weights for policy 0, policy_version 1710 (0.0008)
+[2023-09-11 21:24:56,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 10902.9). Total num frames: 7012352. Throughput: 0: 2875.1. Samples: 746650. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 21:24:56,385][88175] Avg episode reward: [(0, '3.160')]
+[2023-09-11 21:24:59,114][83013] Updated weights for policy 0, policy_version 1720 (0.0009)
+[2023-09-11 21:25:01,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 10913.0). Total num frames: 7069696. Throughput: 0: 2868.0. Samples: 763708. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:25:01,384][88175] Avg episode reward: [(0, '3.340')]
+[2023-09-11 21:25:02,683][83013] Updated weights for policy 0, policy_version 1730 (0.0009)
+[2023-09-11 21:25:05,845][83013] Updated weights for policy 0, policy_version 1740 (0.0009)
+[2023-09-11 21:25:06,384][88175] Fps is (10 sec: 12287.9, 60 sec: 11605.3, 300 sec: 10951.5). Total num frames: 7135232. Throughput: 0: 2862.3. Samples: 772136. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:25:06,386][88175] Avg episode reward: [(0, '3.150')]
+[2023-09-11 21:25:08,345][83013] Updated weights for policy 0, policy_version 1750 (0.0010)
+[2023-09-11 21:25:10,852][83013] Updated weights for policy 0, policy_version 1760 (0.0009)
+[2023-09-11 21:25:11,383][88175] Fps is (10 sec: 14745.5, 60 sec: 12014.9, 300 sec: 11045.2). Total num frames: 7217152. Throughput: 0: 3002.0. Samples: 795470. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:25:11,384][88175] Avg episode reward: [(0, '3.000')]
+[2023-09-11 21:25:13,357][83013] Updated weights for policy 0, policy_version 1770 (0.0008)
+[2023-09-11 21:25:15,858][83013] Updated weights for policy 0, policy_version 1780 (0.0009)
+[2023-09-11 21:25:16,383][88175] Fps is (10 sec: 16384.4, 60 sec: 12424.5, 300 sec: 11135.7). Total num frames: 7299072. Throughput: 0: 3164.0. Samples: 819928. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:25:16,384][88175] Avg episode reward: [(0, '3.290')]
+[2023-09-11 21:25:18,350][83013] Updated weights for policy 0, policy_version 1790 (0.0008)
+[2023-09-11 21:25:20,785][83013] Updated weights for policy 0, policy_version 1800 (0.0008)
+[2023-09-11 21:25:21,384][88175] Fps is (10 sec: 16383.8, 60 sec: 12834.1, 300 sec: 11413.3). Total num frames: 7380992. Throughput: 0: 3248.0. Samples: 832424. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:25:21,385][88175] Avg episode reward: [(0, '3.420')]
+[2023-09-11 21:25:23,275][83013] Updated weights for policy 0, policy_version 1810 (0.0008)
+[2023-09-11 21:25:25,750][83013] Updated weights for policy 0, policy_version 1820 (0.0012)
+[2023-09-11 21:25:26,383][88175] Fps is (10 sec: 16384.0, 60 sec: 13243.8, 300 sec: 11607.8). Total num frames: 7462912. Throughput: 0: 3415.3. Samples: 857370. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:25:26,384][88175] Avg episode reward: [(0, '3.290')]
+[2023-09-11 21:25:28,221][83013] Updated weights for policy 0, policy_version 1830 (0.0008)
+[2023-09-11 21:25:30,701][83013] Updated weights for policy 0, policy_version 1840 (0.0008)
+[2023-09-11 21:25:31,383][88175] Fps is (10 sec: 16384.3, 60 sec: 13653.3, 300 sec: 11760.4). Total num frames: 7544832. Throughput: 0: 3587.1. Samples: 882250. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:25:31,384][88175] Avg episode reward: [(0, '3.190')]
+[2023-09-11 21:25:33,234][83013] Updated weights for policy 0, policy_version 1850 (0.0010)
+[2023-09-11 21:25:35,695][83013] Updated weights for policy 0, policy_version 1860 (0.0009)
+[2023-09-11 21:25:36,383][88175] Fps is (10 sec: 16383.8, 60 sec: 14062.9, 300 sec: 11913.1). Total num frames: 7626752. Throughput: 0: 3665.1. Samples: 894508. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:25:36,385][88175] Avg episode reward: [(0, '3.190')]
+[2023-09-11 21:25:38,219][83013] Updated weights for policy 0, policy_version 1870 (0.0010)
+[2023-09-11 21:25:40,661][83013] Updated weights for policy 0, policy_version 1880 (0.0010)
+[2023-09-11 21:25:41,383][88175] Fps is (10 sec: 16383.8, 60 sec: 14472.5, 300 sec: 12038.1). Total num frames: 7708672. Throughput: 0: 3833.0. Samples: 919136. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:25:41,385][88175] Avg episode reward: [(0, '3.300')]
+[2023-09-11 21:25:43,558][83013] Updated weights for policy 0, policy_version 1890 (0.0008)
+[2023-09-11 21:25:46,383][88175] Fps is (10 sec: 14745.7, 60 sec: 14609.1, 300 sec: 12135.3). Total num frames: 7774208. Throughput: 0: 3903.1. Samples: 939348. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:25:46,385][88175] Avg episode reward: [(0, '3.280')]
+[2023-09-11 21:25:47,103][83013] Updated weights for policy 0, policy_version 1900 (0.0009)
+[2023-09-11 21:25:50,697][83013] Updated weights for policy 0, policy_version 1910 (0.0009)
+[2023-09-11 21:25:51,384][88175] Fps is (10 sec: 12287.8, 60 sec: 14609.1, 300 sec: 12190.8). Total num frames: 7831552. Throughput: 0: 3904.4. Samples: 947832. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:25:51,385][88175] Avg episode reward: [(0, '3.160')]
+[2023-09-11 21:25:54,259][83013] Updated weights for policy 0, policy_version 1920 (0.0009)
+[2023-09-11 21:25:56,383][88175] Fps is (10 sec: 11059.2, 60 sec: 14540.8, 300 sec: 12218.6). Total num frames: 7884800. Throughput: 0: 3774.1. Samples: 965304. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:25:56,386][88175] Avg episode reward: [(0, '3.520')]
+[2023-09-11 21:25:57,806][83013] Updated weights for policy 0, policy_version 1930 (0.0008)
+[2023-09-11 21:26:01,383][88175] Fps is (10 sec: 11059.4, 60 sec: 14540.8, 300 sec: 12288.0). Total num frames: 7942144. Throughput: 0: 3611.0. Samples: 982424. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:26:01,385][88175] Avg episode reward: [(0, '3.230')]
+[2023-09-11 21:26:01,420][83013] Updated weights for policy 0, policy_version 1940 (0.0009)
+[2023-09-11 21:26:05,023][83013] Updated weights for policy 0, policy_version 1950 (0.0008)
+[2023-09-11 21:26:06,384][88175] Fps is (10 sec: 11468.6, 60 sec: 14404.3, 300 sec: 12357.4). Total num frames: 7999488. Throughput: 0: 3520.6. Samples: 990852. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:26:06,385][88175] Avg episode reward: [(0, '3.400')]
+[2023-09-11 21:26:06,414][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001954_8003584.pth...
+[2023-09-11 21:26:06,481][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001231_5042176.pth
+[2023-09-11 21:26:08,522][83013] Updated weights for policy 0, policy_version 1960 (0.0008)
+[2023-09-11 21:26:11,384][88175] Fps is (10 sec: 11468.6, 60 sec: 13994.6, 300 sec: 12413.0). Total num frames: 8056832. Throughput: 0: 3351.5. Samples: 1008188. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:26:11,385][88175] Avg episode reward: [(0, '3.320')]
+[2023-09-11 21:26:12,060][83013] Updated weights for policy 0, policy_version 1970 (0.0009)
+[2023-09-11 21:26:15,604][83013] Updated weights for policy 0, policy_version 1980 (0.0010)
+[2023-09-11 21:26:16,384][88175] Fps is (10 sec: 11878.5, 60 sec: 13653.3, 300 sec: 12440.7). Total num frames: 8118272. Throughput: 0: 3186.5. Samples: 1025642. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:26:16,385][88175] Avg episode reward: [(0, '3.340')]
+[2023-09-11 21:26:19,199][83013] Updated weights for policy 0, policy_version 1990 (0.0008)
+[2023-09-11 21:26:21,383][88175] Fps is (10 sec: 11878.6, 60 sec: 13243.8, 300 sec: 12454.6). Total num frames: 8175616. Throughput: 0: 3103.4. Samples: 1034160. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:26:21,384][88175] Avg episode reward: [(0, '3.300')]
+[2023-09-11 21:26:22,803][83013] Updated weights for policy 0, policy_version 2000 (0.0008)
+[2023-09-11 21:26:26,352][83013] Updated weights for policy 0, policy_version 2010 (0.0008)
+[2023-09-11 21:26:26,383][88175] Fps is (10 sec: 11469.0, 60 sec: 12834.1, 300 sec: 12426.8). Total num frames: 8232960. Throughput: 0: 2938.7. Samples: 1051378. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:26:26,385][88175] Avg episode reward: [(0, '3.400')]
+[2023-09-11 21:26:30,010][83013] Updated weights for policy 0, policy_version 2020 (0.0008)
+[2023-09-11 21:26:31,384][88175] Fps is (10 sec: 11059.0, 60 sec: 12356.2, 300 sec: 12357.4). Total num frames: 8286208. Throughput: 0: 2866.6. Samples: 1068346. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:26:31,385][88175] Avg episode reward: [(0, '3.170')]
+[2023-09-11 21:26:33,621][83013] Updated weights for policy 0, policy_version 2030 (0.0009)
+[2023-09-11 21:26:36,383][88175] Fps is (10 sec: 11059.2, 60 sec: 11946.7, 300 sec: 12329.7). Total num frames: 8343552. Throughput: 0: 2868.7. Samples: 1076924. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:26:36,384][88175] Avg episode reward: [(0, '3.310')]
+[2023-09-11 21:26:37,216][83013] Updated weights for policy 0, policy_version 2040 (0.0008)
+[2023-09-11 21:26:40,765][83013] Updated weights for policy 0, policy_version 2050 (0.0008)
+[2023-09-11 21:26:41,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.0, 300 sec: 12329.6). Total num frames: 8400896. Throughput: 0: 2860.1. Samples: 1094010. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:26:41,385][88175] Avg episode reward: [(0, '3.220')]
+[2023-09-11 21:26:44,319][83013] Updated weights for policy 0, policy_version 2060 (0.0008)
+[2023-09-11 21:26:46,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11400.5, 300 sec: 12315.8). Total num frames: 8458240. Throughput: 0: 2865.0. Samples: 1111350. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:26:46,385][88175] Avg episode reward: [(0, '3.290')]
+[2023-09-11 21:26:47,897][83013] Updated weights for policy 0, policy_version 2070 (0.0008)
+[2023-09-11 21:26:51,374][83013] Updated weights for policy 0, policy_version 2080 (0.0009)
+[2023-09-11 21:26:51,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11468.8, 300 sec: 12301.9). Total num frames: 8519680. Throughput: 0: 2866.9. Samples: 1119860. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:26:51,385][88175] Avg episode reward: [(0, '3.410')]
+[2023-09-11 21:26:54,985][83013] Updated weights for policy 0, policy_version 2090 (0.0009)
+[2023-09-11 21:26:56,384][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12232.5). Total num frames: 8572928. Throughput: 0: 2866.5. Samples: 1137182. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:26:56,385][88175] Avg episode reward: [(0, '3.080')]
+[2023-09-11 21:26:58,535][83013] Updated weights for policy 0, policy_version 2100 (0.0008)
+[2023-09-11 21:27:01,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.1, 300 sec: 12190.8). Total num frames: 8634368. Throughput: 0: 2862.7. Samples: 1154462. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:27:01,385][88175] Avg episode reward: [(0, '3.580')]
+[2023-09-11 21:27:02,050][83013] Updated weights for policy 0, policy_version 2110 (0.0011)
+[2023-09-11 21:27:05,634][83013] Updated weights for policy 0, policy_version 2120 (0.0008)
+[2023-09-11 21:27:06,384][88175] Fps is (10 sec: 11878.4, 60 sec: 11537.1, 300 sec: 12121.4). Total num frames: 8691712. Throughput: 0: 2863.4. Samples: 1163014. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:27:06,385][88175] Avg episode reward: [(0, '3.910')]
+[2023-09-11 21:27:09,249][83013] Updated weights for policy 0, policy_version 2130 (0.0008)
+[2023-09-11 21:27:11,383][88175] Fps is (10 sec: 11059.3, 60 sec: 11468.8, 300 sec: 12052.0). Total num frames: 8744960. Throughput: 0: 2865.2. Samples: 1180314. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:27:11,384][88175] Avg episode reward: [(0, '4.000')]
+[2023-09-11 21:27:12,841][83013] Updated weights for policy 0, policy_version 2140 (0.0008)
+[2023-09-11 21:27:16,366][83013] Updated weights for policy 0, policy_version 2150 (0.0008)
+[2023-09-11 21:27:16,384][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12065.8). Total num frames: 8806400. Throughput: 0: 2871.0. Samples: 1197540. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:27:16,385][88175] Avg episode reward: [(0, '4.000')]
+[2023-09-11 21:27:19,949][83013] Updated weights for policy 0, policy_version 2160 (0.0008)
+[2023-09-11 21:27:21,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11468.8, 300 sec: 12065.8). Total num frames: 8863744. Throughput: 0: 2866.5. Samples: 1205916. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 21:27:21,385][88175] Avg episode reward: [(0, '4.700')]
+[2023-09-11 21:27:23,442][83013] Updated weights for policy 0, policy_version 2170 (0.0008)
+[2023-09-11 21:27:26,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12065.8). Total num frames: 8921088. Throughput: 0: 2874.1. Samples: 1223342. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:27:26,385][88175] Avg episode reward: [(0, '4.480')]
+[2023-09-11 21:27:27,081][83013] Updated weights for policy 0, policy_version 2180 (0.0009)
+[2023-09-11 21:27:30,550][83013] Updated weights for policy 0, policy_version 2190 (0.0008)
+[2023-09-11 21:27:31,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12052.0). Total num frames: 8978432. Throughput: 0: 2876.5. Samples: 1240792. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:27:31,384][88175] Avg episode reward: [(0, '4.950')]
+[2023-09-11 21:27:34,111][83013] Updated weights for policy 0, policy_version 2200 (0.0008)
+[2023-09-11 21:27:36,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.0, 300 sec: 12052.0). Total num frames: 9035776. Throughput: 0: 2879.6. Samples: 1249444. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:27:36,385][88175] Avg episode reward: [(0, '5.130')]
+[2023-09-11 21:27:37,653][83013] Updated weights for policy 0, policy_version 2210 (0.0008)
+[2023-09-11 21:27:41,184][83013] Updated weights for policy 0, policy_version 2220 (0.0008)
+[2023-09-11 21:27:41,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11537.1, 300 sec: 12038.1). Total num frames: 9093120. Throughput: 0: 2882.8. Samples: 1266908. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:27:41,385][88175] Avg episode reward: [(0, '5.360')]
+[2023-09-11 21:27:44,730][83013] Updated weights for policy 0, policy_version 2230 (0.0009)
+[2023-09-11 21:27:46,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12038.1). Total num frames: 9150464. Throughput: 0: 2880.4. Samples: 1284082. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:27:46,385][88175] Avg episode reward: [(0, '5.480')]
+[2023-09-11 21:27:48,256][83013] Updated weights for policy 0, policy_version 2240 (0.0010)
+[2023-09-11 21:27:51,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12038.1). Total num frames: 9207808. Throughput: 0: 2881.0. Samples: 1292658. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 21:27:51,385][88175] Avg episode reward: [(0, '5.900')]
+[2023-09-11 21:27:51,889][83013] Updated weights for policy 0, policy_version 2250 (0.0008)
+[2023-09-11 21:27:55,521][83013] Updated weights for policy 0, policy_version 2260 (0.0008)
+[2023-09-11 21:27:56,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11537.1, 300 sec: 12038.1). Total num frames: 9265152. Throughput: 0: 2873.7. Samples: 1309630. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:27:56,385][88175] Avg episode reward: [(0, '5.590')]
+[2023-09-11 21:27:59,110][83013] Updated weights for policy 0, policy_version 2270 (0.0008)
+[2023-09-11 21:28:01,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12121.4). Total num frames: 9322496. Throughput: 0: 2870.8. Samples: 1326724. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:28:01,384][88175] Avg episode reward: [(0, '6.050')]
+[2023-09-11 21:28:02,646][83013] Updated weights for policy 0, policy_version 2280 (0.0008)
+[2023-09-11 21:28:06,152][83013] Updated weights for policy 0, policy_version 2290 (0.0008)
+[2023-09-11 21:28:06,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 9379840. Throughput: 0: 2876.7. Samples: 1335368. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:28:06,385][88175] Avg episode reward: [(0, '6.110')]
+[2023-09-11 21:28:06,390][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002290_9379840.pth...
+[2023-09-11 21:28:06,443][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001572_6438912.pth
+[2023-09-11 21:28:09,605][83013] Updated weights for policy 0, policy_version 2300 (0.0009)
+[2023-09-11 21:28:11,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11537.0, 300 sec: 12107.5). Total num frames: 9437184. Throughput: 0: 2885.3. Samples: 1353180. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:28:11,385][88175] Avg episode reward: [(0, '6.030')]
+[2023-09-11 21:28:13,200][83013] Updated weights for policy 0, policy_version 2310 (0.0009)
+[2023-09-11 21:28:16,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 9494528. Throughput: 0: 2880.3. Samples: 1370406. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:28:16,386][88175] Avg episode reward: [(0, '6.210')]
+[2023-09-11 21:28:16,796][83013] Updated weights for policy 0, policy_version 2320 (0.0009)
+[2023-09-11 21:28:20,344][83013] Updated weights for policy 0, policy_version 2330 (0.0008)
+[2023-09-11 21:28:21,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 9551872. Throughput: 0: 2878.1. Samples: 1378960. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:28:21,385][88175] Avg episode reward: [(0, '6.390')]
+[2023-09-11 21:28:23,950][83013] Updated weights for policy 0, policy_version 2340 (0.0009)
+[2023-09-11 21:28:26,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 9609216. Throughput: 0: 2875.7. Samples: 1396312. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:28:26,385][88175] Avg episode reward: [(0, '6.230')]
+[2023-09-11 21:28:27,503][83013] Updated weights for policy 0, policy_version 2350 (0.0011)
+[2023-09-11 21:28:30,531][83013] Updated weights for policy 0, policy_version 2360 (0.0009)
+[2023-09-11 21:28:31,383][88175] Fps is (10 sec: 12697.9, 60 sec: 11673.6, 300 sec: 12149.2). Total num frames: 9678848. Throughput: 0: 2917.4. Samples: 1415366. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:28:31,385][88175] Avg episode reward: [(0, '6.330')]
+[2023-09-11 21:28:33,037][83013] Updated weights for policy 0, policy_version 2370 (0.0008)
+[2023-09-11 21:28:35,585][83013] Updated weights for policy 0, policy_version 2380 (0.0008)
+[2023-09-11 21:28:36,383][88175] Fps is (10 sec: 15155.2, 60 sec: 12083.2, 300 sec: 12232.5). Total num frames: 9760768. Throughput: 0: 2996.3. Samples: 1427490. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:28:36,384][88175] Avg episode reward: [(0, '6.440')]
+[2023-09-11 21:28:38,084][83013] Updated weights for policy 0, policy_version 2390 (0.0008)
+[2023-09-11 21:28:40,518][83013] Updated weights for policy 0, policy_version 2400 (0.0008)
+[2023-09-11 21:28:41,383][88175] Fps is (10 sec: 16384.0, 60 sec: 12492.8, 300 sec: 12315.8). Total num frames: 9842688. Throughput: 0: 3171.6. Samples: 1452352. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:28:41,384][88175] Avg episode reward: [(0, '6.650')]
+[2023-09-11 21:28:42,942][83013] Updated weights for policy 0, policy_version 2410 (0.0008)
+[2023-09-11 21:28:45,484][83013] Updated weights for policy 0, policy_version 2420 (0.0009)
+[2023-09-11 21:28:46,384][88175] Fps is (10 sec: 16383.9, 60 sec: 12902.4, 300 sec: 12399.1). Total num frames: 9924608. Throughput: 0: 3346.2. Samples: 1477306. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:28:46,385][88175] Avg episode reward: [(0, '6.500')]
+[2023-09-11 21:28:47,948][83013] Updated weights for policy 0, policy_version 2430 (0.0008)
+[2023-09-11 21:28:50,406][83013] Updated weights for policy 0, policy_version 2440 (0.0008)
+[2023-09-11 21:28:51,384][88175] Fps is (10 sec: 16383.7, 60 sec: 13312.0, 300 sec: 12482.4). Total num frames: 10006528. Throughput: 0: 3425.2. Samples: 1489502. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:28:51,385][88175] Avg episode reward: [(0, '6.710')]
+[2023-09-11 21:28:52,888][83013] Updated weights for policy 0, policy_version 2450 (0.0008)
+[2023-09-11 21:28:55,353][83013] Updated weights for policy 0, policy_version 2460 (0.0009)
+[2023-09-11 21:28:56,383][88175] Fps is (10 sec: 16793.7, 60 sec: 13789.9, 300 sec: 12579.6). Total num frames: 10092544. Throughput: 0: 3576.7. Samples: 1514132. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:28:56,385][88175] Avg episode reward: [(0, '6.940')]
+[2023-09-11 21:28:57,885][83013] Updated weights for policy 0, policy_version 2470 (0.0009)
+[2023-09-11 21:29:00,402][83013] Updated weights for policy 0, policy_version 2480 (0.0008)
+[2023-09-11 21:29:01,384][88175] Fps is (10 sec: 16793.6, 60 sec: 14199.4, 300 sec: 12662.9). Total num frames: 10174464. Throughput: 0: 3740.0. Samples: 1538704. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:01,385][88175] Avg episode reward: [(0, '7.320')]
+[2023-09-11 21:29:02,870][83013] Updated weights for policy 0, policy_version 2490 (0.0008)
+[2023-09-11 21:29:06,035][83013] Updated weights for policy 0, policy_version 2500 (0.0008)
+[2023-09-11 21:29:06,384][88175] Fps is (10 sec: 15154.8, 60 sec: 14404.2, 300 sec: 12704.5). Total num frames: 10244096. Throughput: 0: 3824.7. Samples: 1551070. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:06,386][88175] Avg episode reward: [(0, '7.460')]
+[2023-09-11 21:29:09,760][83013] Updated weights for policy 0, policy_version 2510 (0.0010)
+[2023-09-11 21:29:11,383][88175] Fps is (10 sec: 11878.6, 60 sec: 14267.8, 300 sec: 12676.8). Total num frames: 10293248. Throughput: 0: 3810.9. Samples: 1567802. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:11,385][88175] Avg episode reward: [(0, '7.380')]
+[2023-09-11 21:29:13,741][83013] Updated weights for policy 0, policy_version 2520 (0.0010)
+[2023-09-11 21:29:16,384][88175] Fps is (10 sec: 10649.9, 60 sec: 14267.8, 300 sec: 12676.8). Total num frames: 10350592. Throughput: 0: 3739.6. Samples: 1583650. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:16,385][88175] Avg episode reward: [(0, '7.420')]
+[2023-09-11 21:29:17,452][83013] Updated weights for policy 0, policy_version 2530 (0.0009)
+[2023-09-11 21:29:21,024][83013] Updated weights for policy 0, policy_version 2540 (0.0010)
+[2023-09-11 21:29:21,383][88175] Fps is (10 sec: 11468.7, 60 sec: 14267.8, 300 sec: 12676.8). Total num frames: 10407936. Throughput: 0: 3657.8. Samples: 1592090. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:21,385][88175] Avg episode reward: [(0, '7.300')]
+[2023-09-11 21:29:24,607][83013] Updated weights for policy 0, policy_version 2550 (0.0009)
+[2023-09-11 21:29:26,384][88175] Fps is (10 sec: 11468.5, 60 sec: 14267.7, 300 sec: 12676.8). Total num frames: 10465280. Throughput: 0: 3488.1. Samples: 1609320. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:26,386][88175] Avg episode reward: [(0, '7.550')]
+[2023-09-11 21:29:28,132][83013] Updated weights for policy 0, policy_version 2560 (0.0008)
+[2023-09-11 21:29:31,383][88175] Fps is (10 sec: 11468.9, 60 sec: 14062.9, 300 sec: 12676.8). Total num frames: 10522624. Throughput: 0: 3318.1. Samples: 1626618. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:31,384][88175] Avg episode reward: [(0, '7.780')]
+[2023-09-11 21:29:31,674][83013] Updated weights for policy 0, policy_version 2570 (0.0008)
+[2023-09-11 21:29:35,232][83013] Updated weights for policy 0, policy_version 2580 (0.0009)
+[2023-09-11 21:29:36,383][88175] Fps is (10 sec: 11469.2, 60 sec: 13653.3, 300 sec: 12676.8). Total num frames: 10579968. Throughput: 0: 3240.5. Samples: 1635326. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:36,385][88175] Avg episode reward: [(0, '7.790')]
+[2023-09-11 21:29:38,767][83013] Updated weights for policy 0, policy_version 2590 (0.0008)
+[2023-09-11 21:29:41,383][88175] Fps is (10 sec: 11468.9, 60 sec: 13243.7, 300 sec: 12676.8). Total num frames: 10637312. Throughput: 0: 3077.3. Samples: 1652608. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:41,384][88175] Avg episode reward: [(0, '7.500')]
+[2023-09-11 21:29:42,407][83013] Updated weights for policy 0, policy_version 2600 (0.0009)
+[2023-09-11 21:29:45,929][83013] Updated weights for policy 0, policy_version 2610 (0.0010)
+[2023-09-11 21:29:46,383][88175] Fps is (10 sec: 11468.7, 60 sec: 12834.1, 300 sec: 12676.8). Total num frames: 10694656. Throughput: 0: 2915.2. Samples: 1669890. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:29:46,385][88175] Avg episode reward: [(0, '7.620')]
+[2023-09-11 21:29:49,418][83013] Updated weights for policy 0, policy_version 2620 (0.0008)
+[2023-09-11 21:29:51,384][88175] Fps is (10 sec: 11468.6, 60 sec: 12424.5, 300 sec: 12676.8). Total num frames: 10752000. Throughput: 0: 2831.7. Samples: 1678494. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:51,385][88175] Avg episode reward: [(0, '7.850')]
+[2023-09-11 21:29:53,009][83013] Updated weights for policy 0, policy_version 2630 (0.0008)
+[2023-09-11 21:29:56,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11946.7, 300 sec: 12676.8). Total num frames: 10809344. Throughput: 0: 2840.3. Samples: 1695616. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:29:56,386][88175] Avg episode reward: [(0, '7.840')]
+[2023-09-11 21:29:56,689][83013] Updated weights for policy 0, policy_version 2640 (0.0009)
+[2023-09-11 21:30:00,218][83013] Updated weights for policy 0, policy_version 2650 (0.0008)
+[2023-09-11 21:30:01,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.0, 300 sec: 12649.0). Total num frames: 10866688. Throughput: 0: 2869.1. Samples: 1712760. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:30:01,385][88175] Avg episode reward: [(0, '8.110')]
+[2023-09-11 21:30:03,875][83013] Updated weights for policy 0, policy_version 2660 (0.0008)
+[2023-09-11 21:30:06,384][88175] Fps is (10 sec: 11058.9, 60 sec: 11264.0, 300 sec: 12551.8). Total num frames: 10919936. Throughput: 0: 2866.1. Samples: 1721064. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:30:06,386][88175] Avg episode reward: [(0, '8.010')]
+[2023-09-11 21:30:06,396][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002667_10924032.pth...
+[2023-09-11 21:30:06,449][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001954_8003584.pth
+[2023-09-11 21:30:07,472][83013] Updated weights for policy 0, policy_version 2670 (0.0009)
+[2023-09-11 21:30:11,062][83013] Updated weights for policy 0, policy_version 2680 (0.0008)
+[2023-09-11 21:30:11,384][88175] Fps is (10 sec: 11059.3, 60 sec: 11400.5, 300 sec: 12468.5). Total num frames: 10977280. Throughput: 0: 2865.0. Samples: 1738246. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:30:11,385][88175] Avg episode reward: [(0, '8.100')]
+[2023-09-11 21:30:14,665][83013] Updated weights for policy 0, policy_version 2690 (0.0008)
+[2023-09-11 21:30:16,384][88175] Fps is (10 sec: 11469.2, 60 sec: 11400.5, 300 sec: 12385.2). Total num frames: 11034624. Throughput: 0: 2863.5. Samples: 1755478. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:30:16,386][88175] Avg episode reward: [(0, '8.200')]
+[2023-09-11 21:30:18,187][83013] Updated weights for policy 0, policy_version 2700 (0.0009)
+[2023-09-11 21:30:21,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11400.5, 300 sec: 12301.9). Total num frames: 11091968. Throughput: 0: 2857.9. Samples: 1763934. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:30:21,385][88175] Avg episode reward: [(0, '7.920')]
+[2023-09-11 21:30:21,782][83013] Updated weights for policy 0, policy_version 2710 (0.0008)
+[2023-09-11 21:30:25,345][83013] Updated weights for policy 0, policy_version 2720 (0.0009)
+[2023-09-11 21:30:26,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11400.6, 300 sec: 12218.6). Total num frames: 11149312. Throughput: 0: 2857.9. Samples: 1781212. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:30:26,385][88175] Avg episode reward: [(0, '8.150')]
+[2023-09-11 21:30:28,917][83013] Updated weights for policy 0, policy_version 2730 (0.0009)
+[2023-09-11 21:30:31,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11400.5, 300 sec: 12135.3). Total num frames: 11206656. Throughput: 0: 2854.7. Samples: 1798350. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:30:31,384][88175] Avg episode reward: [(0, '8.250')]
+[2023-09-11 21:30:32,535][83013] Updated weights for policy 0, policy_version 2740 (0.0010)
+[2023-09-11 21:30:35,997][83013] Updated weights for policy 0, policy_version 2750 (0.0010)
+[2023-09-11 21:30:36,383][88175] Fps is (10 sec: 11878.6, 60 sec: 11468.8, 300 sec: 12065.8). Total num frames: 11268096. Throughput: 0: 2856.8. Samples: 1807050. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:30:36,385][88175] Avg episode reward: [(0, '8.420')]
+[2023-09-11 21:30:39,471][83013] Updated weights for policy 0, policy_version 2760 (0.0008)
+[2023-09-11 21:30:41,383][88175] Fps is (10 sec: 11878.2, 60 sec: 11468.8, 300 sec: 12038.1). Total num frames: 11325440. Throughput: 0: 2872.4. Samples: 1824874. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:30:41,385][88175] Avg episode reward: [(0, '8.070')]
+[2023-09-11 21:30:42,923][83013] Updated weights for policy 0, policy_version 2770 (0.0009)
+[2023-09-11 21:30:46,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12038.1). Total num frames: 11382784. Throughput: 0: 2885.9. Samples: 1842626. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:30:46,385][88175] Avg episode reward: [(0, '8.230')]
+[2023-09-11 21:30:46,398][83013] Updated weights for policy 0, policy_version 2780 (0.0008)
+[2023-09-11 21:30:49,841][83013] Updated weights for policy 0, policy_version 2790 (0.0009)
+[2023-09-11 21:30:51,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11537.1, 300 sec: 12065.8). Total num frames: 11444224. Throughput: 0: 2892.6. Samples: 1851228. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:30:51,385][88175] Avg episode reward: [(0, '8.390')]
+[2023-09-11 21:30:53,390][83013] Updated weights for policy 0, policy_version 2800 (0.0008)
+[2023-09-11 21:30:56,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11537.1, 300 sec: 12065.8). Total num frames: 11501568. Throughput: 0: 2895.9. Samples: 1868560. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:30:56,384][88175] Avg episode reward: [(0, '8.430')]
+[2023-09-11 21:30:57,005][83013] Updated weights for policy 0, policy_version 2810 (0.0008)
+[2023-09-11 21:31:00,567][83013] Updated weights for policy 0, policy_version 2820 (0.0010)
+[2023-09-11 21:31:01,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12065.8). Total num frames: 11558912. Throughput: 0: 2900.3. Samples: 1885990. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:31:01,385][88175] Avg episode reward: [(0, '8.500')]
+[2023-09-11 21:31:04,133][83013] Updated weights for policy 0, policy_version 2830 (0.0008)
+[2023-09-11 21:31:06,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11605.4, 300 sec: 12065.8). Total num frames: 11616256. Throughput: 0: 2901.4. Samples: 1894498. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:31:06,385][88175] Avg episode reward: [(0, '8.640')]
+[2023-09-11 21:31:07,709][83013] Updated weights for policy 0, policy_version 2840 (0.0008)
+[2023-09-11 21:31:11,239][83013] Updated weights for policy 0, policy_version 2850 (0.0009)
+[2023-09-11 21:31:11,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11605.4, 300 sec: 12052.0). Total num frames: 11673600. Throughput: 0: 2901.1. Samples: 1911760. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:31:11,385][88175] Avg episode reward: [(0, '8.490')]
+[2023-09-11 21:31:14,866][83013] Updated weights for policy 0, policy_version 2860 (0.0008)
+[2023-09-11 21:31:16,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11605.4, 300 sec: 12052.0). Total num frames: 11730944. Throughput: 0: 2901.2. Samples: 1928904. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:31:16,385][88175] Avg episode reward: [(0, '8.560')]
+[2023-09-11 21:31:18,416][83013] Updated weights for policy 0, policy_version 2870 (0.0009)
+[2023-09-11 21:31:21,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11605.4, 300 sec: 12052.0). Total num frames: 11788288. Throughput: 0: 2900.1. Samples: 1937552. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
+[2023-09-11 21:31:21,385][88175] Avg episode reward: [(0, '8.490')]
+[2023-09-11 21:31:21,936][83013] Updated weights for policy 0, policy_version 2880 (0.0008)
+[2023-09-11 21:31:25,519][83013] Updated weights for policy 0, policy_version 2890 (0.0008)
+[2023-09-11 21:31:26,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11605.4, 300 sec: 12065.8). Total num frames: 11845632. Throughput: 0: 2885.7. Samples: 1954732. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:31:26,385][88175] Avg episode reward: [(0, '8.600')]
+[2023-09-11 21:31:29,070][83013] Updated weights for policy 0, policy_version 2900 (0.0008)
+[2023-09-11 21:31:31,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11605.3, 300 sec: 12065.8). Total num frames: 11902976. Throughput: 0: 2871.8. Samples: 1971858. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:31:31,385][88175] Avg episode reward: [(0, '8.670')]
+[2023-09-11 21:31:32,683][83013] Updated weights for policy 0, policy_version 2910 (0.0008)
+[2023-09-11 21:31:36,323][83013] Updated weights for policy 0, policy_version 2920 (0.0008)
+[2023-09-11 21:31:36,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12065.8). Total num frames: 11960320. Throughput: 0: 2868.1. Samples: 1980294. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:31:36,385][88175] Avg episode reward: [(0, '8.630')]
+[2023-09-11 21:31:39,870][83013] Updated weights for policy 0, policy_version 2930 (0.0009)
+[2023-09-11 21:31:41,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.1, 300 sec: 12065.9). Total num frames: 12017664. Throughput: 0: 2865.2. Samples: 1997496. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:31:41,385][88175] Avg episode reward: [(0, '8.600')]
+[2023-09-11 21:31:43,454][83013] Updated weights for policy 0, policy_version 2940 (0.0009)
+[2023-09-11 21:31:46,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12052.0). Total num frames: 12075008. Throughput: 0: 2863.4. Samples: 2014844. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:31:46,385][88175] Avg episode reward: [(0, '8.460')]
+[2023-09-11 21:31:46,986][83013] Updated weights for policy 0, policy_version 2950 (0.0009)
+[2023-09-11 21:31:50,572][83013] Updated weights for policy 0, policy_version 2960 (0.0009)
+[2023-09-11 21:31:51,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12065.8). Total num frames: 12132352. Throughput: 0: 2867.8. Samples: 2023550. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:31:51,385][88175] Avg episode reward: [(0, '8.720')]
+[2023-09-11 21:31:53,291][83013] Updated weights for policy 0, policy_version 2970 (0.0009)
+[2023-09-11 21:31:55,748][83013] Updated weights for policy 0, policy_version 2980 (0.0009)
+[2023-09-11 21:31:56,383][88175] Fps is (10 sec: 13926.3, 60 sec: 11878.4, 300 sec: 12135.3). Total num frames: 12214272. Throughput: 0: 2964.8. Samples: 2045178. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:31:56,384][88175] Avg episode reward: [(0, '8.960')]
+[2023-09-11 21:31:58,194][83013] Updated weights for policy 0, policy_version 2990 (0.0008)
+[2023-09-11 21:32:00,683][83013] Updated weights for policy 0, policy_version 3000 (0.0008)
+[2023-09-11 21:32:01,383][88175] Fps is (10 sec: 16384.1, 60 sec: 12288.0, 300 sec: 12218.6). Total num frames: 12296192. Throughput: 0: 3139.3. Samples: 2070174. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:32:01,385][88175] Avg episode reward: [(0, '9.010')]
+[2023-09-11 21:32:03,210][83013] Updated weights for policy 0, policy_version 3010 (0.0008)
+[2023-09-11 21:32:05,639][83013] Updated weights for policy 0, policy_version 3020 (0.0008)
+[2023-09-11 21:32:06,384][88175] Fps is (10 sec: 16793.2, 60 sec: 12765.8, 300 sec: 12329.6). Total num frames: 12382208. Throughput: 0: 3220.1. Samples: 2082456. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:32:06,386][88175] Avg episode reward: [(0, '8.930')]
+[2023-09-11 21:32:06,394][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000003023_12382208.pth...
+[2023-09-11 21:32:06,452][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002290_9379840.pth
+[2023-09-11 21:32:08,128][83013] Updated weights for policy 0, policy_version 3030 (0.0010)
+[2023-09-11 21:32:10,642][83013] Updated weights for policy 0, policy_version 3040 (0.0008)
+[2023-09-11 21:32:11,383][88175] Fps is (10 sec: 16384.1, 60 sec: 13107.2, 300 sec: 12385.2). Total num frames: 12460032. Throughput: 0: 3383.0. Samples: 2106966. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:32:11,385][88175] Avg episode reward: [(0, '8.930')]
+[2023-09-11 21:32:13,132][83013] Updated weights for policy 0, policy_version 3050 (0.0008)
+[2023-09-11 21:32:15,637][83013] Updated weights for policy 0, policy_version 3060 (0.0008)
+[2023-09-11 21:32:16,384][88175] Fps is (10 sec: 15974.4, 60 sec: 13516.7, 300 sec: 12468.5). Total num frames: 12541952. Throughput: 0: 3551.4. Samples: 2131672. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:32:16,385][88175] Avg episode reward: [(0, '8.660')]
+[2023-09-11 21:32:18,151][83013] Updated weights for policy 0, policy_version 3070 (0.0008)
+[2023-09-11 21:32:20,609][83013] Updated weights for policy 0, policy_version 3080 (0.0009)
+[2023-09-11 21:32:21,383][88175] Fps is (10 sec: 16793.5, 60 sec: 13994.6, 300 sec: 12565.7). Total num frames: 12627968. Throughput: 0: 3637.2. Samples: 2143970. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:32:21,385][88175] Avg episode reward: [(0, '8.580')]
+[2023-09-11 21:32:23,098][83013] Updated weights for policy 0, policy_version 3090 (0.0008)
+[2023-09-11 21:32:25,591][83013] Updated weights for policy 0, policy_version 3100 (0.0008)
+[2023-09-11 21:32:26,383][88175] Fps is (10 sec: 16794.1, 60 sec: 14404.3, 300 sec: 12649.0). Total num frames: 12709888. Throughput: 0: 3802.8. Samples: 2168620. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:32:26,384][88175] Avg episode reward: [(0, '8.770')]
+[2023-09-11 21:32:28,138][83013] Updated weights for policy 0, policy_version 3110 (0.0008)
+[2023-09-11 21:32:31,383][88175] Fps is (10 sec: 14335.9, 60 sec: 14472.5, 300 sec: 12662.9). Total num frames: 12771328. Throughput: 0: 3883.6. Samples: 2189606. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:32:31,385][88175] Avg episode reward: [(0, '9.080')]
+[2023-09-11 21:32:31,777][83013] Updated weights for policy 0, policy_version 3120 (0.0008)
+[2023-09-11 21:32:35,357][83013] Updated weights for policy 0, policy_version 3130 (0.0008)
+[2023-09-11 21:32:36,384][88175] Fps is (10 sec: 11878.1, 60 sec: 14472.5, 300 sec: 12662.9). Total num frames: 12828672. Throughput: 0: 3880.3. Samples: 2198166. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:32:36,386][88175] Avg episode reward: [(0, '9.200')]
+[2023-09-11 21:32:38,897][83013] Updated weights for policy 0, policy_version 3140 (0.0008)
+[2023-09-11 21:32:41,384][88175] Fps is (10 sec: 11468.7, 60 sec: 14472.5, 300 sec: 12662.9). Total num frames: 12886016. Throughput: 0: 3782.6. Samples: 2215396. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:32:41,385][88175] Avg episode reward: [(0, '9.150')]
+[2023-09-11 21:32:42,441][83013] Updated weights for policy 0, policy_version 3150 (0.0008)
+[2023-09-11 21:32:46,061][83013] Updated weights for policy 0, policy_version 3160 (0.0009)
+[2023-09-11 21:32:46,384][88175] Fps is (10 sec: 11468.7, 60 sec: 14472.5, 300 sec: 12662.9). Total num frames: 12943360. Throughput: 0: 3610.9. Samples: 2232664. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:32:46,385][88175] Avg episode reward: [(0, '9.240')]
+[2023-09-11 21:32:49,636][83013] Updated weights for policy 0, policy_version 3170 (0.0008)
+[2023-09-11 21:32:51,384][88175] Fps is (10 sec: 11468.7, 60 sec: 14472.5, 300 sec: 12662.9). Total num frames: 13000704. Throughput: 0: 3528.1. Samples: 2241222. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:32:51,385][88175] Avg episode reward: [(0, '9.270')]
+[2023-09-11 21:32:53,232][83013] Updated weights for policy 0, policy_version 3180 (0.0008)
+[2023-09-11 21:32:56,384][88175] Fps is (10 sec: 11468.7, 60 sec: 14062.9, 300 sec: 12662.9). Total num frames: 13058048. Throughput: 0: 3364.7. Samples: 2258378. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:32:56,385][88175] Avg episode reward: [(0, '8.940')]
+[2023-09-11 21:32:56,810][83013] Updated weights for policy 0, policy_version 3190 (0.0008)
+[2023-09-11 21:33:00,352][83013] Updated weights for policy 0, policy_version 3200 (0.0009)
+[2023-09-11 21:33:01,384][88175] Fps is (10 sec: 11469.0, 60 sec: 13653.3, 300 sec: 12662.9). Total num frames: 13115392. Throughput: 0: 3196.7. Samples: 2275524. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:33:01,385][88175] Avg episode reward: [(0, '9.120')]
+[2023-09-11 21:33:03,872][83013] Updated weights for policy 0, policy_version 3210 (0.0008)
+[2023-09-11 21:33:06,383][88175] Fps is (10 sec: 11878.7, 60 sec: 13243.8, 300 sec: 12676.8). Total num frames: 13176832. Throughput: 0: 3118.0. Samples: 2284280. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:33:06,385][88175] Avg episode reward: [(0, '9.080')]
+[2023-09-11 21:33:07,390][83013] Updated weights for policy 0, policy_version 3220 (0.0009)
+[2023-09-11 21:33:10,878][83013] Updated weights for policy 0, policy_version 3230 (0.0008)
+[2023-09-11 21:33:11,383][88175] Fps is (10 sec: 11878.6, 60 sec: 12902.4, 300 sec: 12676.8). Total num frames: 13234176. Throughput: 0: 2959.6. Samples: 2301802. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:33:11,384][88175] Avg episode reward: [(0, '9.250')]
+[2023-09-11 21:33:14,496][83013] Updated weights for policy 0, policy_version 3240 (0.0009)
+[2023-09-11 21:33:16,384][88175] Fps is (10 sec: 11468.7, 60 sec: 12492.8, 300 sec: 12676.8). Total num frames: 13291520. Throughput: 0: 2876.0. Samples: 2319028. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:33:16,385][88175] Avg episode reward: [(0, '8.990')]
+[2023-09-11 21:33:18,067][83013] Updated weights for policy 0, policy_version 3250 (0.0008)
+[2023-09-11 21:33:21,383][88175] Fps is (10 sec: 11468.8, 60 sec: 12014.9, 300 sec: 12676.8). Total num frames: 13348864. Throughput: 0: 2876.6. Samples: 2327614. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 21:33:21,384][88175] Avg episode reward: [(0, '8.960')]
+[2023-09-11 21:33:21,637][83013] Updated weights for policy 0, policy_version 3260 (0.0008)
+[2023-09-11 21:33:25,232][83013] Updated weights for policy 0, policy_version 3270 (0.0009)
+[2023-09-11 21:33:26,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11605.3, 300 sec: 12635.1). Total num frames: 13406208. Throughput: 0: 2870.8. Samples: 2344580. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:33:26,385][88175] Avg episode reward: [(0, '8.950')]
+[2023-09-11 21:33:28,856][83013] Updated weights for policy 0, policy_version 3280 (0.0009)
+[2023-09-11 21:33:31,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12551.8). Total num frames: 13463552. Throughput: 0: 2872.8. Samples: 2361938. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:33:31,385][88175] Avg episode reward: [(0, '9.080')]
+[2023-09-11 21:33:32,388][83013] Updated weights for policy 0, policy_version 3290 (0.0009)
+[2023-09-11 21:33:35,953][83013] Updated weights for policy 0, policy_version 3300 (0.0010)
+[2023-09-11 21:33:36,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.1, 300 sec: 12468.5). Total num frames: 13520896. Throughput: 0: 2872.5. Samples: 2370484. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:33:36,385][88175] Avg episode reward: [(0, '9.030')]
+[2023-09-11 21:33:39,421][83013] Updated weights for policy 0, policy_version 3310 (0.0008)
+[2023-09-11 21:33:41,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12385.2). Total num frames: 13578240. Throughput: 0: 2880.1. Samples: 2387984. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:33:41,385][88175] Avg episode reward: [(0, '8.870')]
+[2023-09-11 21:33:42,924][83013] Updated weights for policy 0, policy_version 3320 (0.0008)
+[2023-09-11 21:33:46,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12301.9). Total num frames: 13635584. Throughput: 0: 2885.7. Samples: 2405382. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:33:46,386][88175] Avg episode reward: [(0, '8.790')]
+[2023-09-11 21:33:46,507][83013] Updated weights for policy 0, policy_version 3330 (0.0008)
+[2023-09-11 21:33:50,131][83013] Updated weights for policy 0, policy_version 3340 (0.0008)
+[2023-09-11 21:33:51,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11537.1, 300 sec: 12204.7). Total num frames: 13692928. Throughput: 0: 2884.4. Samples: 2414076. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
+[2023-09-11 21:33:51,385][88175] Avg episode reward: [(0, '8.920')]
+[2023-09-11 21:33:53,615][83013] Updated weights for policy 0, policy_version 3350 (0.0008)
+[2023-09-11 21:33:56,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11537.1, 300 sec: 12121.4). Total num frames: 13750272. Throughput: 0: 2877.0. Samples: 2431268. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:33:56,385][88175] Avg episode reward: [(0, '8.990')]
+[2023-09-11 21:33:57,215][83013] Updated weights for policy 0, policy_version 3360 (0.0008)
+[2023-09-11 21:34:00,709][83013] Updated weights for policy 0, policy_version 3370 (0.0009)
+[2023-09-11 21:34:01,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11605.4, 300 sec: 12093.6). Total num frames: 13811712. Throughput: 0: 2878.5. Samples: 2448558. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:34:01,384][88175] Avg episode reward: [(0, '8.740')]
+[2023-09-11 21:34:04,225][83013] Updated weights for policy 0, policy_version 3380 (0.0008)
+[2023-09-11 21:34:06,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11537.1, 300 sec: 12121.4). Total num frames: 13869056. Throughput: 0: 2880.5. Samples: 2457238. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:34:06,385][88175] Avg episode reward: [(0, '8.650')]
+[2023-09-11 21:34:06,393][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000003386_13869056.pth...
+[2023-09-11 21:34:06,443][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002667_10924032.pth
+[2023-09-11 21:34:07,768][83013] Updated weights for policy 0, policy_version 3390 (0.0009)
+[2023-09-11 21:34:11,341][83013] Updated weights for policy 0, policy_version 3400 (0.0008)
+[2023-09-11 21:34:11,383][88175] Fps is (10 sec: 11468.6, 60 sec: 11537.0, 300 sec: 12121.4). Total num frames: 13926400. Throughput: 0: 2888.1. Samples: 2474546. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:34:11,385][88175] Avg episode reward: [(0, '8.350')]
+[2023-09-11 21:34:14,894][83013] Updated weights for policy 0, policy_version 3410 (0.0008)
+[2023-09-11 21:34:16,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11537.1, 300 sec: 12121.4). Total num frames: 13983744. Throughput: 0: 2888.3. Samples: 2491912. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 21:34:16,385][88175] Avg episode reward: [(0, '8.570')]
+[2023-09-11 21:34:18,543][83013] Updated weights for policy 0, policy_version 3420 (0.0009)
+[2023-09-11 21:34:21,384][88175] Fps is (10 sec: 11059.2, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 14036992. Throughput: 0: 2885.0. Samples: 2500310. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:34:21,385][88175] Avg episode reward: [(0, '8.370')]
+[2023-09-11 21:34:22,181][83013] Updated weights for policy 0, policy_version 3430 (0.0008)
+[2023-09-11 21:34:25,736][83013] Updated weights for policy 0, policy_version 3440 (0.0009)
+[2023-09-11 21:34:26,383][88175] Fps is (10 sec: 11059.5, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 14094336. Throughput: 0: 2876.1. Samples: 2517406. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:34:26,385][88175] Avg episode reward: [(0, '8.460')]
+[2023-09-11 21:34:29,306][83013] Updated weights for policy 0, policy_version 3450 (0.0008)
+[2023-09-11 21:34:31,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11537.1, 300 sec: 12121.4). Total num frames: 14155776. Throughput: 0: 2873.2. Samples: 2534676. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:34:31,385][88175] Avg episode reward: [(0, '8.650')]
+[2023-09-11 21:34:32,746][83013] Updated weights for policy 0, policy_version 3460 (0.0009)
+[2023-09-11 21:34:36,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 14209024. Throughput: 0: 2869.7. Samples: 2543214. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:34:36,386][88175] Avg episode reward: [(0, '8.790')]
+[2023-09-11 21:34:36,387][83013] Updated weights for policy 0, policy_version 3470 (0.0008)
+[2023-09-11 21:34:40,000][83013] Updated weights for policy 0, policy_version 3480 (0.0009)
+[2023-09-11 21:34:41,384][88175] Fps is (10 sec: 11058.9, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 14266368. Throughput: 0: 2869.0. Samples: 2560374. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:34:41,386][88175] Avg episode reward: [(0, '8.770')]
+[2023-09-11 21:34:43,544][83013] Updated weights for policy 0, policy_version 3490 (0.0008)
+[2023-09-11 21:34:46,383][88175] Fps is (10 sec: 11878.6, 60 sec: 11537.1, 300 sec: 12121.4). Total num frames: 14327808. Throughput: 0: 2876.0. Samples: 2577980. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:34:46,385][88175] Avg episode reward: [(0, '8.600')]
+[2023-09-11 21:34:47,064][83013] Updated weights for policy 0, policy_version 3500 (0.0008)
+[2023-09-11 21:34:50,625][83013] Updated weights for policy 0, policy_version 3510 (0.0008)
+[2023-09-11 21:34:51,384][88175] Fps is (10 sec: 11878.5, 60 sec: 11537.0, 300 sec: 12121.4). Total num frames: 14385152. Throughput: 0: 2875.0. Samples: 2586614. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:34:51,385][88175] Avg episode reward: [(0, '8.980')]
+[2023-09-11 21:34:54,190][83013] Updated weights for policy 0, policy_version 3520 (0.0008)
+[2023-09-11 21:34:56,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.0, 300 sec: 12121.4). Total num frames: 14442496. Throughput: 0: 2870.4. Samples: 2603716. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:34:56,385][88175] Avg episode reward: [(0, '8.350')]
+[2023-09-11 21:34:57,781][83013] Updated weights for policy 0, policy_version 3530 (0.0008)
+[2023-09-11 21:35:01,383][88175] Fps is (10 sec: 11059.4, 60 sec: 11400.5, 300 sec: 12121.4). Total num frames: 14495744. Throughput: 0: 2864.8. Samples: 2620826. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:35:01,386][88175] Avg episode reward: [(0, '8.510')]
+[2023-09-11 21:35:01,394][83013] Updated weights for policy 0, policy_version 3540 (0.0009)
+[2023-09-11 21:35:04,962][83013] Updated weights for policy 0, policy_version 3550 (0.0009)
+[2023-09-11 21:35:06,383][88175] Fps is (10 sec: 11059.3, 60 sec: 11400.5, 300 sec: 12121.4). Total num frames: 14553088. Throughput: 0: 2865.1. Samples: 2629238. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:35:06,385][88175] Avg episode reward: [(0, '8.660')]
+[2023-09-11 21:35:08,559][83013] Updated weights for policy 0, policy_version 3560 (0.0009)
+[2023-09-11 21:35:11,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11400.5, 300 sec: 12121.4). Total num frames: 14610432. Throughput: 0: 2869.4. Samples: 2646528. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:35:11,385][88175] Avg episode reward: [(0, '8.590')]
+[2023-09-11 21:35:12,181][83013] Updated weights for policy 0, policy_version 3570 (0.0009)
+[2023-09-11 21:35:15,312][83013] Updated weights for policy 0, policy_version 3580 (0.0008)
+[2023-09-11 21:35:16,383][88175] Fps is (10 sec: 12697.7, 60 sec: 11605.4, 300 sec: 12163.0). Total num frames: 14680064. Throughput: 0: 2907.4. Samples: 2665510. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:35:16,385][88175] Avg episode reward: [(0, '8.530')]
+[2023-09-11 21:35:17,791][83013] Updated weights for policy 0, policy_version 3590 (0.0008)
+[2023-09-11 21:35:20,269][83013] Updated weights for policy 0, policy_version 3600 (0.0008)
+[2023-09-11 21:35:21,383][88175] Fps is (10 sec: 15155.2, 60 sec: 12083.2, 300 sec: 12246.4). Total num frames: 14761984. Throughput: 0: 2996.5. Samples: 2678056. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:35:21,385][88175] Avg episode reward: [(0, '8.410')]
+[2023-09-11 21:35:22,732][83013] Updated weights for policy 0, policy_version 3610 (0.0008)
+[2023-09-11 21:35:25,237][83013] Updated weights for policy 0, policy_version 3620 (0.0009)
+[2023-09-11 21:35:26,383][88175] Fps is (10 sec: 16383.9, 60 sec: 12492.8, 300 sec: 12329.7). Total num frames: 14843904. Throughput: 0: 3161.3. Samples: 2702632. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:35:26,385][88175] Avg episode reward: [(0, '8.820')]
+[2023-09-11 21:35:27,751][83013] Updated weights for policy 0, policy_version 3630 (0.0009)
+[2023-09-11 21:35:30,207][83013] Updated weights for policy 0, policy_version 3640 (0.0008)
+[2023-09-11 21:35:31,383][88175] Fps is (10 sec: 16384.1, 60 sec: 12834.1, 300 sec: 12399.1). Total num frames: 14925824. Throughput: 0: 3325.6. Samples: 2727630. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:35:31,385][88175] Avg episode reward: [(0, '8.420')]
+[2023-09-11 21:35:32,730][83013] Updated weights for policy 0, policy_version 3650 (0.0010)
+[2023-09-11 21:35:35,217][83013] Updated weights for policy 0, policy_version 3660 (0.0007)
+[2023-09-11 21:35:36,384][88175] Fps is (10 sec: 16383.9, 60 sec: 13312.0, 300 sec: 12482.4). Total num frames: 15007744. Throughput: 0: 3404.2. Samples: 2739802. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
+[2023-09-11 21:35:36,385][88175] Avg episode reward: [(0, '8.440')]
+[2023-09-11 21:35:37,719][83013] Updated weights for policy 0, policy_version 3670 (0.0008)
+[2023-09-11 21:35:40,183][83013] Updated weights for policy 0, policy_version 3680 (0.0008)
+[2023-09-11 21:35:41,383][88175] Fps is (10 sec: 16793.7, 60 sec: 13789.9, 300 sec: 12579.6). Total num frames: 15093760. Throughput: 0: 3574.8. Samples: 2764580. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:35:41,384][88175] Avg episode reward: [(0, '8.740')]
+[2023-09-11 21:35:42,551][83013] Updated weights for policy 0, policy_version 3690 (0.0008)
+[2023-09-11 21:35:45,094][83013] Updated weights for policy 0, policy_version 3700 (0.0009)
+[2023-09-11 21:35:46,384][88175] Fps is (10 sec: 16793.5, 60 sec: 14131.2, 300 sec: 12649.0). Total num frames: 15175680. Throughput: 0: 3737.1. Samples: 2788994. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
+[2023-09-11 21:35:46,385][88175] Avg episode reward: [(0, '8.710')]
+[2023-09-11 21:35:47,617][83013] Updated weights for policy 0, policy_version 3710 (0.0009)
+[2023-09-11 21:35:50,108][83013] Updated weights for policy 0, policy_version 3720 (0.0009)
+[2023-09-11 21:35:51,383][88175] Fps is (10 sec: 16383.9, 60 sec: 14540.8, 300 sec: 12732.3). Total num frames: 15257600. Throughput: 0: 3823.4. Samples: 2801292. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:35:51,385][88175] Avg episode reward: [(0, '8.700')]
+[2023-09-11 21:35:52,576][83013] Updated weights for policy 0, policy_version 3730 (0.0009)
+[2023-09-11 21:35:55,027][83013] Updated weights for policy 0, policy_version 3740 (0.0008)
+[2023-09-11 21:35:56,383][88175] Fps is (10 sec: 16384.3, 60 sec: 14950.4, 300 sec: 12815.6). Total num frames: 15339520. Throughput: 0: 3995.8. Samples: 2826338. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:35:56,384][88175] Avg episode reward: [(0, '8.730')]
+[2023-09-11 21:35:57,502][83013] Updated weights for policy 0, policy_version 3750 (0.0008)
+[2023-09-11 21:36:00,970][83013] Updated weights for policy 0, policy_version 3760 (0.0008)
+[2023-09-11 21:36:01,383][88175] Fps is (10 sec: 14745.7, 60 sec: 15155.2, 300 sec: 12843.4). Total num frames: 15405056. Throughput: 0: 4038.2. Samples: 2847228. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:36:01,385][88175] Avg episode reward: [(0, '8.690')]
+[2023-09-11 21:36:04,552][83013] Updated weights for policy 0, policy_version 3770 (0.0008)
+[2023-09-11 21:36:06,384][88175] Fps is (10 sec: 12287.6, 60 sec: 15155.2, 300 sec: 12843.4). Total num frames: 15462400. Throughput: 0: 3949.8. Samples: 2855800. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:36:06,386][88175] Avg episode reward: [(0, '8.780')]
+[2023-09-11 21:36:06,394][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000003775_15462400.pth...
+[2023-09-11 21:36:06,443][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000003023_12382208.pth
+[2023-09-11 21:36:08,150][83013] Updated weights for policy 0, policy_version 3780 (0.0009)
+[2023-09-11 21:36:11,384][88175] Fps is (10 sec: 11468.5, 60 sec: 15155.2, 300 sec: 12843.4). Total num frames: 15519744. Throughput: 0: 3785.3. Samples: 2872972. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:36:11,385][88175] Avg episode reward: [(0, '8.690')]
+[2023-09-11 21:36:11,711][83013] Updated weights for policy 0, policy_version 3790 (0.0008)
+[2023-09-11 21:36:15,174][83013] Updated weights for policy 0, policy_version 3800 (0.0008)
+[2023-09-11 21:36:16,384][88175] Fps is (10 sec: 11468.9, 60 sec: 14950.3, 300 sec: 12843.4). Total num frames: 15577088. Throughput: 0: 3614.9. Samples: 2890300. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
+[2023-09-11 21:36:16,385][88175] Avg episode reward: [(0, '8.730')]
+[2023-09-11 21:36:18,789][83013] Updated weights for policy 0, policy_version 3810 (0.0008)
+[2023-09-11 21:36:21,384][88175] Fps is (10 sec: 11468.8, 60 sec: 14540.8, 300 sec: 12843.4). Total num frames: 15634432. Throughput: 0: 3533.5. Samples: 2898812. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:36:21,385][88175] Avg episode reward: [(0, '8.620')]
+[2023-09-11 21:36:22,381][83013] Updated weights for policy 0, policy_version 3820 (0.0008)
+[2023-09-11 21:36:26,002][83013] Updated weights for policy 0, policy_version 3830 (0.0009)
+[2023-09-11 21:36:26,383][88175] Fps is (10 sec: 11468.9, 60 sec: 14131.2, 300 sec: 12843.4). Total num frames: 15691776. Throughput: 0: 3367.2. Samples: 2916106. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:36:26,385][88175] Avg episode reward: [(0, '8.800')]
+[2023-09-11 21:36:29,492][83013] Updated weights for policy 0, policy_version 3840 (0.0009)
+[2023-09-11 21:36:31,383][88175] Fps is (10 sec: 11469.1, 60 sec: 13721.6, 300 sec: 12843.4). Total num frames: 15749120. Throughput: 0: 3207.9. Samples: 2933348. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:36:31,385][88175] Avg episode reward: [(0, '8.710')]
+[2023-09-11 21:36:33,112][83013] Updated weights for policy 0, policy_version 3850 (0.0008)
+[2023-09-11 21:36:36,383][88175] Fps is (10 sec: 11468.8, 60 sec: 13312.0, 300 sec: 12843.4). Total num frames: 15806464. Throughput: 0: 3121.8. Samples: 2941774. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:36:36,385][88175] Avg episode reward: [(0, '8.630')]
+[2023-09-11 21:36:36,601][83013] Updated weights for policy 0, policy_version 3860 (0.0009)
+[2023-09-11 21:36:40,193][83013] Updated weights for policy 0, policy_version 3870 (0.0008)
+[2023-09-11 21:36:41,384][88175] Fps is (10 sec: 11468.5, 60 sec: 12834.1, 300 sec: 12843.4). Total num frames: 15863808. Throughput: 0: 2953.8. Samples: 2959258. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:36:41,385][88175] Avg episode reward: [(0, '8.740')]
+[2023-09-11 21:36:43,829][83013] Updated weights for policy 0, policy_version 3880 (0.0008)
+[2023-09-11 21:36:46,384][88175] Fps is (10 sec: 11468.6, 60 sec: 12424.5, 300 sec: 12843.4). Total num frames: 15921152. Throughput: 0: 2872.1. Samples: 2976474. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:36:46,385][88175] Avg episode reward: [(0, '8.770')]
+[2023-09-11 21:36:47,341][83013] Updated weights for policy 0, policy_version 3890 (0.0009)
+[2023-09-11 21:36:50,883][83013] Updated weights for policy 0, policy_version 3900 (0.0008)
+[2023-09-11 21:36:51,383][88175] Fps is (10 sec: 11469.1, 60 sec: 12014.9, 300 sec: 12760.1). Total num frames: 15978496. Throughput: 0: 2872.2. Samples: 2985048. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:36:51,384][88175] Avg episode reward: [(0, '8.670')]
+[2023-09-11 21:36:54,510][83013] Updated weights for policy 0, policy_version 3910 (0.0008)
+[2023-09-11 21:36:56,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11605.3, 300 sec: 12676.8). Total num frames: 16035840. Throughput: 0: 2870.2. Samples: 3002132. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:36:56,385][88175] Avg episode reward: [(0, '9.030')]
+[2023-09-11 21:36:58,029][83013] Updated weights for policy 0, policy_version 3920 (0.0009)
+[2023-09-11 21:37:01,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12579.6). Total num frames: 16093184. Throughput: 0: 2870.2. Samples: 3019458. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:37:01,385][88175] Avg episode reward: [(0, '9.180')]
+[2023-09-11 21:37:01,582][83013] Updated weights for policy 0, policy_version 3930 (0.0008)
+[2023-09-11 21:37:05,151][83013] Updated weights for policy 0, policy_version 3940 (0.0008)
+[2023-09-11 21:37:06,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 12510.1). Total num frames: 16150528. Throughput: 0: 2873.6. Samples: 3028124. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:37:06,385][88175] Avg episode reward: [(0, '8.990')]
+[2023-09-11 21:37:08,727][83013] Updated weights for policy 0, policy_version 3950 (0.0008)
+[2023-09-11 21:37:11,383][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 12426.9). Total num frames: 16207872. Throughput: 0: 2875.6. Samples: 3045510. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:37:11,385][88175] Avg episode reward: [(0, '8.940')]
+[2023-09-11 21:37:12,246][83013] Updated weights for policy 0, policy_version 3960 (0.0009)
+[2023-09-11 21:37:15,834][83013] Updated weights for policy 0, policy_version 3970 (0.0008)
+[2023-09-11 21:37:16,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12329.7). Total num frames: 16265216. Throughput: 0: 2873.4. Samples: 3062650. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:37:16,385][88175] Avg episode reward: [(0, '9.040')]
+[2023-09-11 21:37:19,461][83013] Updated weights for policy 0, policy_version 3980 (0.0008)
+[2023-09-11 21:37:21,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12246.3). Total num frames: 16322560. Throughput: 0: 2877.1. Samples: 3071244. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:37:21,385][88175] Avg episode reward: [(0, '8.870')]
+[2023-09-11 21:37:23,038][83013] Updated weights for policy 0, policy_version 3990 (0.0009)
+[2023-09-11 21:37:26,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12232.5). Total num frames: 16379904. Throughput: 0: 2867.1. Samples: 3088276. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
+[2023-09-11 21:37:26,385][88175] Avg episode reward: [(0, '8.930')]
+[2023-09-11 21:37:26,640][83013] Updated weights for policy 0, policy_version 4000 (0.0008)
+[2023-09-11 21:37:30,190][83013] Updated weights for policy 0, policy_version 4010 (0.0008)
+[2023-09-11 21:37:31,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12232.5). Total num frames: 16437248. Throughput: 0: 2868.8. Samples: 3105568. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:37:31,385][88175] Avg episode reward: [(0, '9.050')]
+[2023-09-11 21:37:33,790][83013] Updated weights for policy 0, policy_version 4020 (0.0008)
+[2023-09-11 21:37:36,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12232.5). Total num frames: 16494592. Throughput: 0: 2867.1. Samples: 3114068. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:37:36,385][88175] Avg episode reward: [(0, '8.780')]
+[2023-09-11 21:37:37,376][83013] Updated weights for policy 0, policy_version 4030 (0.0008)
+[2023-09-11 21:37:40,959][83013] Updated weights for policy 0, policy_version 4040 (0.0009)
+[2023-09-11 21:37:41,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12232.5). Total num frames: 16551936. Throughput: 0: 2869.2. Samples: 3131244. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:37:41,385][88175] Avg episode reward: [(0, '8.700')]
+[2023-09-11 21:37:44,442][83013] Updated weights for policy 0, policy_version 4050 (0.0009)
+[2023-09-11 21:37:46,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12232.5). Total num frames: 16609280. Throughput: 0: 2867.8. Samples: 3148510. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:37:46,384][88175] Avg episode reward: [(0, '8.640')]
+[2023-09-11 21:37:48,081][83013] Updated weights for policy 0, policy_version 4060 (0.0009)
+[2023-09-11 21:37:51,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12232.5). Total num frames: 16666624. Throughput: 0: 2866.4. Samples: 3157112. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:37:51,385][88175] Avg episode reward: [(0, '9.010')]
+[2023-09-11 21:37:51,583][83013] Updated weights for policy 0, policy_version 4070 (0.0009)
+[2023-09-11 21:37:55,151][83013] Updated weights for policy 0, policy_version 4080 (0.0009)
+[2023-09-11 21:37:56,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12232.5). Total num frames: 16723968. Throughput: 0: 2860.8. Samples: 3174246. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:37:56,385][88175] Avg episode reward: [(0, '9.030')]
+[2023-09-11 21:37:58,751][83013] Updated weights for policy 0, policy_version 4090 (0.0009)
+[2023-09-11 21:38:01,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 16781312. Throughput: 0: 2862.2. Samples: 3191450. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:38:01,385][88175] Avg episode reward: [(0, '9.140')]
+[2023-09-11 21:38:02,364][83013] Updated weights for policy 0, policy_version 4100 (0.0008)
+[2023-09-11 21:38:05,941][83013] Updated weights for policy 0, policy_version 4110 (0.0009)
+[2023-09-11 21:38:06,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 16838656. Throughput: 0: 2860.0. Samples: 3199944. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:38:06,386][88175] Avg episode reward: [(0, '9.150')]
+[2023-09-11 21:38:06,393][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000004111_16838656.pth...
+[2023-09-11 21:38:06,446][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000003386_13869056.pth
+[2023-09-11 21:38:09,364][83013] Updated weights for policy 0, policy_version 4120 (0.0008)
+[2023-09-11 21:38:11,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 16896000. Throughput: 0: 2874.5. Samples: 3217628. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:38:11,385][88175] Avg episode reward: [(0, '9.340')]
+[2023-09-11 21:38:12,894][83013] Updated weights for policy 0, policy_version 4130 (0.0010)
+[2023-09-11 21:38:16,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 16953344. Throughput: 0: 2871.2. Samples: 3234772. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:38:16,385][88175] Avg episode reward: [(0, '9.200')]
+[2023-09-11 21:38:16,493][83013] Updated weights for policy 0, policy_version 4140 (0.0008)
+[2023-09-11 21:38:20,070][83013] Updated weights for policy 0, policy_version 4150 (0.0008)
+[2023-09-11 21:38:21,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 17010688. Throughput: 0: 2878.7. Samples: 3243610. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:38:21,385][88175] Avg episode reward: [(0, '9.110')]
+[2023-09-11 21:38:23,700][83013] Updated weights for policy 0, policy_version 4160 (0.0008)
+[2023-09-11 21:38:26,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 17068032. Throughput: 0: 2870.1. Samples: 3260400. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:38:26,384][88175] Avg episode reward: [(0, '9.200')]
+[2023-09-11 21:38:27,298][83013] Updated weights for policy 0, policy_version 4170 (0.0009)
+[2023-09-11 21:38:30,885][83013] Updated weights for policy 0, policy_version 4180 (0.0008)
+[2023-09-11 21:38:31,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 17125376. Throughput: 0: 2867.1. Samples: 3277530. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:38:31,384][88175] Avg episode reward: [(0, '9.300')]
+[2023-09-11 21:38:34,460][83013] Updated weights for policy 0, policy_version 4190 (0.0008)
+[2023-09-11 21:38:36,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 17182720. Throughput: 0: 2869.0. Samples: 3286218. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:38:36,385][88175] Avg episode reward: [(0, '9.220')]
+[2023-09-11 21:38:38,002][83013] Updated weights for policy 0, policy_version 4200 (0.0008)
+[2023-09-11 21:38:41,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 17240064. Throughput: 0: 2871.1. Samples: 3303444. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:38:41,385][88175] Avg episode reward: [(0, '9.490')]
+[2023-09-11 21:38:41,584][83013] Updated weights for policy 0, policy_version 4210 (0.0008)
+[2023-09-11 21:38:44,883][83013] Updated weights for policy 0, policy_version 4220 (0.0008)
+[2023-09-11 21:38:46,384][88175] Fps is (10 sec: 12697.4, 60 sec: 11673.5, 300 sec: 12260.2). Total num frames: 17309696. Throughput: 0: 2910.6. Samples: 3322426. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:38:46,385][88175] Avg episode reward: [(0, '9.460')]
+[2023-09-11 21:38:47,361][83013] Updated weights for policy 0, policy_version 4230 (0.0009)
+[2023-09-11 21:38:49,818][83013] Updated weights for policy 0, policy_version 4240 (0.0008)
+[2023-09-11 21:38:51,383][88175] Fps is (10 sec: 15155.4, 60 sec: 12083.2, 300 sec: 12343.5). Total num frames: 17391616. Throughput: 0: 2999.9. Samples: 3334938. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:38:51,384][88175] Avg episode reward: [(0, '9.420')]
+[2023-09-11 21:38:52,355][83013] Updated weights for policy 0, policy_version 4250 (0.0008)
+[2023-09-11 21:38:54,808][83013] Updated weights for policy 0, policy_version 4260 (0.0008)
+[2023-09-11 21:38:56,383][88175] Fps is (10 sec: 16384.5, 60 sec: 12492.8, 300 sec: 12413.0). Total num frames: 17473536. Throughput: 0: 3159.0. Samples: 3359782. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:38:56,384][88175] Avg episode reward: [(0, '9.410')]
+[2023-09-11 21:38:57,297][83013] Updated weights for policy 0, policy_version 4270 (0.0009)
+[2023-09-11 21:38:59,721][83013] Updated weights for policy 0, policy_version 4280 (0.0009)
+[2023-09-11 21:39:01,383][88175] Fps is (10 sec: 16384.0, 60 sec: 12902.4, 300 sec: 12496.3). Total num frames: 17555456. Throughput: 0: 3334.8. Samples: 3384836. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:39:01,384][88175] Avg episode reward: [(0, '9.500')]
+[2023-09-11 21:39:02,191][83013] Updated weights for policy 0, policy_version 4290 (0.0009)
+[2023-09-11 21:39:04,646][83013] Updated weights for policy 0, policy_version 4300 (0.0008)
+[2023-09-11 21:39:06,383][88175] Fps is (10 sec: 16793.6, 60 sec: 13380.3, 300 sec: 12593.5). Total num frames: 17641472. Throughput: 0: 3411.5. Samples: 3397128. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:39:06,385][88175] Avg episode reward: [(0, '9.240')]
+[2023-09-11 21:39:07,131][83013] Updated weights for policy 0, policy_version 4310 (0.0008)
+[2023-09-11 21:39:09,849][83013] Updated weights for policy 0, policy_version 4320 (0.0008)
+[2023-09-11 21:39:11,383][88175] Fps is (10 sec: 15564.9, 60 sec: 13585.1, 300 sec: 12635.1). Total num frames: 17711104. Throughput: 0: 3568.7. Samples: 3420992. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
+[2023-09-11 21:39:11,385][88175] Avg episode reward: [(0, '9.570')]
+[2023-09-11 21:39:13,420][83013] Updated weights for policy 0, policy_version 4330 (0.0008)
+[2023-09-11 21:39:16,383][88175] Fps is (10 sec: 12697.5, 60 sec: 13585.1, 300 sec: 12649.0). Total num frames: 17768448. Throughput: 0: 3572.8. Samples: 3438308. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:39:16,385][88175] Avg episode reward: [(0, '9.350')]
+[2023-09-11 21:39:16,944][83013] Updated weights for policy 0, policy_version 4340 (0.0009)
+[2023-09-11 21:39:20,515][83013] Updated weights for policy 0, policy_version 4350 (0.0009)
+[2023-09-11 21:39:21,383][88175] Fps is (10 sec: 11468.7, 60 sec: 13585.1, 300 sec: 12649.0). Total num frames: 17825792. Throughput: 0: 3576.1. Samples: 3447142. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:39:21,385][88175] Avg episode reward: [(0, '9.270')]
+[2023-09-11 21:39:24,073][83013] Updated weights for policy 0, policy_version 4360 (0.0008)
+[2023-09-11 21:39:26,384][88175] Fps is (10 sec: 11468.7, 60 sec: 13585.0, 300 sec: 12635.1). Total num frames: 17883136. Throughput: 0: 3572.5. Samples: 3464206. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:39:26,385][88175] Avg episode reward: [(0, '9.380')]
+[2023-09-11 21:39:27,529][83013] Updated weights for policy 0, policy_version 4370 (0.0008)
+[2023-09-11 21:39:31,112][83013] Updated weights for policy 0, policy_version 4380 (0.0008)
+[2023-09-11 21:39:31,383][88175] Fps is (10 sec: 11468.7, 60 sec: 13585.0, 300 sec: 12649.0). Total num frames: 17940480. Throughput: 0: 3538.7. Samples: 3481666. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:39:31,385][88175] Avg episode reward: [(0, '9.250')]
+[2023-09-11 21:39:34,663][83013] Updated weights for policy 0, policy_version 4390 (0.0008)
+[2023-09-11 21:39:36,384][88175] Fps is (10 sec: 11468.6, 60 sec: 13585.0, 300 sec: 12649.0). Total num frames: 17997824. Throughput: 0: 3452.9. Samples: 3490320. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:39:36,386][88175] Avg episode reward: [(0, '9.280')]
+[2023-09-11 21:39:38,237][83013] Updated weights for policy 0, policy_version 4400 (0.0008)
+[2023-09-11 21:39:41,384][88175] Fps is (10 sec: 11468.7, 60 sec: 13585.0, 300 sec: 12635.1). Total num frames: 18055168. Throughput: 0: 3285.8. Samples: 3507642. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:39:41,385][88175] Avg episode reward: [(0, '9.330')]
+[2023-09-11 21:39:41,788][83013] Updated weights for policy 0, policy_version 4410 (0.0008)
+[2023-09-11 21:39:45,339][83013] Updated weights for policy 0, policy_version 4420 (0.0008)
+[2023-09-11 21:39:46,384][88175] Fps is (10 sec: 11468.9, 60 sec: 13380.3, 300 sec: 12635.1). Total num frames: 18112512. Throughput: 0: 3114.9. Samples: 3525008. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:39:46,385][88175] Avg episode reward: [(0, '9.440')]
+[2023-09-11 21:39:48,842][83013] Updated weights for policy 0, policy_version 4430 (0.0008)
+[2023-09-11 21:39:51,383][88175] Fps is (10 sec: 11878.6, 60 sec: 13038.9, 300 sec: 12649.0). Total num frames: 18173952. Throughput: 0: 3033.9. Samples: 3533652. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:39:51,385][88175] Avg episode reward: [(0, '9.460')]
+[2023-09-11 21:39:52,351][83013] Updated weights for policy 0, policy_version 4440 (0.0008)
+[2023-09-11 21:39:55,965][83013] Updated weights for policy 0, policy_version 4450 (0.0008)
+[2023-09-11 21:39:56,383][88175] Fps is (10 sec: 11878.6, 60 sec: 12629.3, 300 sec: 12662.9). Total num frames: 18231296. Throughput: 0: 2885.7. Samples: 3550848. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:39:56,385][88175] Avg episode reward: [(0, '9.560')]
+[2023-09-11 21:39:59,547][83013] Updated weights for policy 0, policy_version 4460 (0.0008)
+[2023-09-11 21:40:01,383][88175] Fps is (10 sec: 11468.7, 60 sec: 12219.7, 300 sec: 12662.9). Total num frames: 18288640. Throughput: 0: 2886.0. Samples: 3568176. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:40:01,385][88175] Avg episode reward: [(0, '9.320')]
+[2023-09-11 21:40:03,129][83013] Updated weights for policy 0, policy_version 4470 (0.0008)
+[2023-09-11 21:40:06,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11741.8, 300 sec: 12662.9). Total num frames: 18345984. Throughput: 0: 2874.5. Samples: 3576494. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:40:06,385][88175] Avg episode reward: [(0, '9.430')]
+[2023-09-11 21:40:06,392][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000004479_18345984.pth...
+[2023-09-11 21:40:06,444][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000003775_15462400.pth
+[2023-09-11 21:40:06,743][83013] Updated weights for policy 0, policy_version 4480 (0.0009)
+[2023-09-11 21:40:10,244][83013] Updated weights for policy 0, policy_version 4490 (0.0009)
+[2023-09-11 21:40:11,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11537.0, 300 sec: 12621.2). Total num frames: 18403328. Throughput: 0: 2880.5. Samples: 3593828. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:40:11,385][88175] Avg episode reward: [(0, '9.510')]
+[2023-09-11 21:40:13,809][83013] Updated weights for policy 0, policy_version 4500 (0.0008)
+[2023-09-11 21:40:16,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11537.1, 300 sec: 12537.9). Total num frames: 18460672. Throughput: 0: 2879.5. Samples: 3611244. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 21:40:16,385][88175] Avg episode reward: [(0, '9.670')]
+[2023-09-11 21:40:17,373][83013] Updated weights for policy 0, policy_version 4510 (0.0010)
+[2023-09-11 21:40:20,944][83013] Updated weights for policy 0, policy_version 4520 (0.0009)
+[2023-09-11 21:40:21,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11537.1, 300 sec: 12454.6). Total num frames: 18518016. Throughput: 0: 2878.1. Samples: 3619834. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:40:21,384][88175] Avg episode reward: [(0, '9.430')]
+[2023-09-11 21:40:24,547][83013] Updated weights for policy 0, policy_version 4530 (0.0008)
+[2023-09-11 21:40:26,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11537.1, 300 sec: 12371.3). Total num frames: 18575360. Throughput: 0: 2873.3. Samples: 3636942. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:40:26,385][88175] Avg episode reward: [(0, '9.390')]
+[2023-09-11 21:40:28,056][83013] Updated weights for policy 0, policy_version 4540 (0.0008)
+[2023-09-11 21:40:31,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12288.0). Total num frames: 18632704. Throughput: 0: 2872.5. Samples: 3654272. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:40:31,385][88175] Avg episode reward: [(0, '9.410')]
+[2023-09-11 21:40:31,576][83013] Updated weights for policy 0, policy_version 4550 (0.0008)
+[2023-09-11 21:40:35,202][83013] Updated weights for policy 0, policy_version 4560 (0.0008)
+[2023-09-11 21:40:36,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11537.1, 300 sec: 12190.8). Total num frames: 18690048. Throughput: 0: 2871.1. Samples: 3662854. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:40:36,385][88175] Avg episode reward: [(0, '9.490')]
+[2023-09-11 21:40:38,757][83013] Updated weights for policy 0, policy_version 4570 (0.0009)
+[2023-09-11 21:40:41,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12107.5). Total num frames: 18747392. Throughput: 0: 2872.4. Samples: 3680104. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:40:41,384][88175] Avg episode reward: [(0, '9.650')]
+[2023-09-11 21:40:42,268][83013] Updated weights for policy 0, policy_version 4580 (0.0009)
+[2023-09-11 21:40:45,834][83013] Updated weights for policy 0, policy_version 4590 (0.0009)
+[2023-09-11 21:40:46,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11537.1, 300 sec: 12024.2). Total num frames: 18804736. Throughput: 0: 2874.6. Samples: 3697532. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:40:46,385][88175] Avg episode reward: [(0, '9.690')]
+[2023-09-11 21:40:49,352][83013] Updated weights for policy 0, policy_version 4600 (0.0009)
+[2023-09-11 21:40:51,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 11940.9). Total num frames: 18862080. Throughput: 0: 2888.5. Samples: 3706474. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:40:51,384][88175] Avg episode reward: [(0, '9.550')]
+[2023-09-11 21:40:52,906][83013] Updated weights for policy 0, policy_version 4610 (0.0008)
+[2023-09-11 21:40:56,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 18919424. Throughput: 0: 2879.3. Samples: 3723394. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:40:56,385][88175] Avg episode reward: [(0, '9.580')]
+[2023-09-11 21:40:56,511][83013] Updated weights for policy 0, policy_version 4620 (0.0008)
+[2023-09-11 21:41:00,080][83013] Updated weights for policy 0, policy_version 4630 (0.0009)
+[2023-09-11 21:41:01,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 18976768. Throughput: 0: 2874.3. Samples: 3740588. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:41:01,385][88175] Avg episode reward: [(0, '9.630')]
+[2023-09-11 21:41:03,651][83013] Updated weights for policy 0, policy_version 4640 (0.0009)
+[2023-09-11 21:41:06,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.9, 300 sec: 11913.1). Total num frames: 19034112. Throughput: 0: 2873.1. Samples: 3749122. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:41:06,384][88175] Avg episode reward: [(0, '9.690')]
+[2023-09-11 21:41:07,238][83013] Updated weights for policy 0, policy_version 4650 (0.0009)
+[2023-09-11 21:41:10,836][83013] Updated weights for policy 0, policy_version 4660 (0.0008)
+[2023-09-11 21:41:11,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 19091456. Throughput: 0: 2876.8. Samples: 3766398. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:41:11,385][88175] Avg episode reward: [(0, '9.900')]
+[2023-09-11 21:41:14,445][83013] Updated weights for policy 0, policy_version 4670 (0.0010)
+[2023-09-11 21:41:16,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 19148800. Throughput: 0: 2868.7. Samples: 3783364. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:41:16,385][88175] Avg episode reward: [(0, '9.910')]
+[2023-09-11 21:41:18,016][83013] Updated weights for policy 0, policy_version 4680 (0.0008)
+[2023-09-11 21:41:21,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 19206144. Throughput: 0: 2871.4. Samples: 3792064. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:41:21,385][88175] Avg episode reward: [(0, '9.440')]
+[2023-09-11 21:41:21,617][83013] Updated weights for policy 0, policy_version 4690 (0.0008)
+[2023-09-11 21:41:25,182][83013] Updated weights for policy 0, policy_version 4700 (0.0008)
+[2023-09-11 21:41:26,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 19263488. Throughput: 0: 2865.9. Samples: 3809070. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:41:26,385][88175] Avg episode reward: [(0, '9.420')]
+[2023-09-11 21:41:28,768][83013] Updated weights for policy 0, policy_version 4710 (0.0008)
+[2023-09-11 21:41:31,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 19320832. Throughput: 0: 2861.2. Samples: 3826284. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:41:31,384][88175] Avg episode reward: [(0, '9.780')]
+[2023-09-11 21:41:32,398][83013] Updated weights for policy 0, policy_version 4720 (0.0009)
+[2023-09-11 21:41:35,880][83013] Updated weights for policy 0, policy_version 4730 (0.0008)
+[2023-09-11 21:41:36,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 19378176. Throughput: 0: 2849.2. Samples: 3834688. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:41:36,386][88175] Avg episode reward: [(0, '9.670')]
+[2023-09-11 21:41:39,481][83013] Updated weights for policy 0, policy_version 4740 (0.0009)
+[2023-09-11 21:41:41,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 19435520. Throughput: 0: 2860.9. Samples: 3852136. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:41:41,384][88175] Avg episode reward: [(0, '9.670')]
+[2023-09-11 21:41:43,080][83013] Updated weights for policy 0, policy_version 4750 (0.0009)
+[2023-09-11 21:41:46,384][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 19492864. Throughput: 0: 2863.1. Samples: 3869428. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:41:46,386][88175] Avg episode reward: [(0, '9.630')]
+[2023-09-11 21:41:46,597][83013] Updated weights for policy 0, policy_version 4760 (0.0008)
+[2023-09-11 21:41:50,128][83013] Updated weights for policy 0, policy_version 4770 (0.0009)
+[2023-09-11 21:41:51,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 11913.1). Total num frames: 19550208. Throughput: 0: 2865.7. Samples: 3878078. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:41:51,385][88175] Avg episode reward: [(0, '9.600')]
+[2023-09-11 21:41:53,758][83013] Updated weights for policy 0, policy_version 4780 (0.0008)
+[2023-09-11 21:41:56,384][88175] Fps is (10 sec: 12288.0, 60 sec: 11605.3, 300 sec: 11940.9). Total num frames: 19615744. Throughput: 0: 2865.4. Samples: 3895342. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:41:56,385][88175] Avg episode reward: [(0, '9.670')]
+[2023-09-11 21:41:56,543][83013] Updated weights for policy 0, policy_version 4790 (0.0007)
+[2023-09-11 21:41:59,011][83013] Updated weights for policy 0, policy_version 4800 (0.0008)
+[2023-09-11 21:42:01,383][88175] Fps is (10 sec: 14745.7, 60 sec: 12015.0, 300 sec: 12024.2). Total num frames: 19697664. Throughput: 0: 3038.4. Samples: 3920090. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:42:01,385][88175] Avg episode reward: [(0, '9.590')]
+[2023-09-11 21:42:01,536][83013] Updated weights for policy 0, policy_version 4810 (0.0008)
+[2023-09-11 21:42:03,972][83013] Updated weights for policy 0, policy_version 4820 (0.0008)
+[2023-09-11 21:42:06,383][88175] Fps is (10 sec: 16384.3, 60 sec: 12424.5, 300 sec: 12107.5). Total num frames: 19779584. Throughput: 0: 3121.8. Samples: 3932544. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:42:06,385][88175] Avg episode reward: [(0, '9.800')]
+[2023-09-11 21:42:06,390][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000004829_19779584.pth...
+[2023-09-11 21:42:06,441][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000004111_16838656.pth
+[2023-09-11 21:42:06,521][83013] Updated weights for policy 0, policy_version 4830 (0.0008)
+[2023-09-11 21:42:08,936][83013] Updated weights for policy 0, policy_version 4840 (0.0008)
+[2023-09-11 21:42:11,383][88175] Fps is (10 sec: 16384.1, 60 sec: 12834.1, 300 sec: 12190.8). Total num frames: 19861504. Throughput: 0: 3296.5. Samples: 3957412. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:42:11,385][88175] Avg episode reward: [(0, '9.730')]
+[2023-09-11 21:42:11,425][83013] Updated weights for policy 0, policy_version 4850 (0.0008)
+[2023-09-11 21:42:13,896][83013] Updated weights for policy 0, policy_version 4860 (0.0008)
+[2023-09-11 21:42:16,384][88175] Fps is (10 sec: 16383.8, 60 sec: 13243.7, 300 sec: 12274.1). Total num frames: 19943424. Throughput: 0: 3460.8. Samples: 3982022. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:42:16,385][88175] Avg episode reward: [(0, '9.580')]
+[2023-09-11 21:42:16,436][83013] Updated weights for policy 0, policy_version 4870 (0.0009)
+[2023-09-11 21:42:18,891][83013] Updated weights for policy 0, policy_version 4880 (0.0008)
+[2023-09-11 21:42:21,383][83013] Updated weights for policy 0, policy_version 4890 (0.0008)
+[2023-09-11 21:42:21,384][88175] Fps is (10 sec: 16793.2, 60 sec: 13721.5, 300 sec: 12371.3). Total num frames: 20029440. Throughput: 0: 3546.0. Samples: 3994260. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:42:21,385][88175] Avg episode reward: [(0, '9.730')]
+[2023-09-11 21:42:23,850][83013] Updated weights for policy 0, policy_version 4900 (0.0008)
+[2023-09-11 21:42:26,318][83013] Updated weights for policy 0, policy_version 4910 (0.0008)
+[2023-09-11 21:42:26,383][88175] Fps is (10 sec: 16793.8, 60 sec: 14131.2, 300 sec: 12454.6). Total num frames: 20111360. Throughput: 0: 3711.4. Samples: 4019150. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:42:26,384][88175] Avg episode reward: [(0, '9.670')]
+[2023-09-11 21:42:28,793][83013] Updated weights for policy 0, policy_version 4920 (0.0008)
+[2023-09-11 21:42:31,295][83013] Updated weights for policy 0, policy_version 4930 (0.0008)
+[2023-09-11 21:42:31,383][88175] Fps is (10 sec: 16384.4, 60 sec: 14540.8, 300 sec: 12537.9). Total num frames: 20193280. Throughput: 0: 3876.8. Samples: 4043884. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:42:31,384][88175] Avg episode reward: [(0, '9.880')]
+[2023-09-11 21:42:33,797][83013] Updated weights for policy 0, policy_version 4940 (0.0008)
+[2023-09-11 21:42:36,317][83013] Updated weights for policy 0, policy_version 4950 (0.0008)
+[2023-09-11 21:42:36,383][88175] Fps is (10 sec: 16384.0, 60 sec: 14950.5, 300 sec: 12621.2). Total num frames: 20275200. Throughput: 0: 3958.6. Samples: 4056216. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:42:36,384][88175] Avg episode reward: [(0, '9.850')]
+[2023-09-11 21:42:38,760][83013] Updated weights for policy 0, policy_version 4960 (0.0008)
+[2023-09-11 21:42:41,384][88175] Fps is (10 sec: 15974.0, 60 sec: 15291.7, 300 sec: 12690.7). Total num frames: 20353024. Throughput: 0: 4127.1. Samples: 4081060. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:42:41,385][88175] Avg episode reward: [(0, '9.840')]
+[2023-09-11 21:42:41,628][83013] Updated weights for policy 0, policy_version 4970 (0.0009)
+[2023-09-11 21:42:45,156][83013] Updated weights for policy 0, policy_version 4980 (0.0009)
+[2023-09-11 21:42:46,383][88175] Fps is (10 sec: 13516.7, 60 sec: 15291.8, 300 sec: 12690.7). Total num frames: 20410368. Throughput: 0: 3969.2. Samples: 4098702. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:42:46,385][88175] Avg episode reward: [(0, '9.820')]
+[2023-09-11 21:42:48,771][83013] Updated weights for policy 0, policy_version 4990 (0.0009)
+[2023-09-11 21:42:51,383][88175] Fps is (10 sec: 11469.0, 60 sec: 15291.7, 300 sec: 12690.7). Total num frames: 20467712. Throughput: 0: 3883.2. Samples: 4107290. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:42:51,385][88175] Avg episode reward: [(0, '9.720')]
+[2023-09-11 21:42:52,377][83013] Updated weights for policy 0, policy_version 5000 (0.0009)
+[2023-09-11 21:42:55,902][83013] Updated weights for policy 0, policy_version 5010 (0.0009)
+[2023-09-11 21:42:56,384][88175] Fps is (10 sec: 11468.5, 60 sec: 15155.2, 300 sec: 12690.6). Total num frames: 20525056. Throughput: 0: 3715.2. Samples: 4124596. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:42:56,386][88175] Avg episode reward: [(0, '9.740')]
+[2023-09-11 21:42:59,498][83013] Updated weights for policy 0, policy_version 5020 (0.0010)
+[2023-09-11 21:43:01,383][88175] Fps is (10 sec: 11468.8, 60 sec: 14745.6, 300 sec: 12690.7). Total num frames: 20582400. Throughput: 0: 3550.3. Samples: 4141786. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:43:01,385][88175] Avg episode reward: [(0, '9.560')]
+[2023-09-11 21:43:02,980][83013] Updated weights for policy 0, policy_version 5030 (0.0009)
+[2023-09-11 21:43:06,384][88175] Fps is (10 sec: 11468.8, 60 sec: 14335.9, 300 sec: 12690.6). Total num frames: 20639744. Throughput: 0: 3469.7. Samples: 4150396. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:43:06,385][88175] Avg episode reward: [(0, '9.630')]
+[2023-09-11 21:43:06,578][83013] Updated weights for policy 0, policy_version 5040 (0.0008)
+[2023-09-11 21:43:10,051][83013] Updated weights for policy 0, policy_version 5050 (0.0008)
+[2023-09-11 21:43:11,384][88175] Fps is (10 sec: 11468.6, 60 sec: 13926.3, 300 sec: 12690.7). Total num frames: 20697088. Throughput: 0: 3309.9. Samples: 4168096. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:43:11,385][88175] Avg episode reward: [(0, '9.710')]
+[2023-09-11 21:43:13,650][83013] Updated weights for policy 0, policy_version 5060 (0.0008)
+[2023-09-11 21:43:16,383][88175] Fps is (10 sec: 11469.1, 60 sec: 13516.8, 300 sec: 12690.7). Total num frames: 20754432. Throughput: 0: 3144.0. Samples: 4185364. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:43:16,385][88175] Avg episode reward: [(0, '9.620')]
+[2023-09-11 21:43:17,153][83013] Updated weights for policy 0, policy_version 5070 (0.0008)
+[2023-09-11 21:43:20,760][83013] Updated weights for policy 0, policy_version 5080 (0.0008)
+[2023-09-11 21:43:21,383][88175] Fps is (10 sec: 11469.0, 60 sec: 13039.0, 300 sec: 12690.7). Total num frames: 20811776. Throughput: 0: 3060.2. Samples: 4193926. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:43:21,385][88175] Avg episode reward: [(0, '9.540')]
+[2023-09-11 21:43:24,340][83013] Updated weights for policy 0, policy_version 5090 (0.0008)
+[2023-09-11 21:43:26,384][88175] Fps is (10 sec: 11468.5, 60 sec: 12629.3, 300 sec: 12690.6). Total num frames: 20869120. Throughput: 0: 2890.1. Samples: 4211116. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:43:26,385][88175] Avg episode reward: [(0, '9.600')]
+[2023-09-11 21:43:27,807][83013] Updated weights for policy 0, policy_version 5100 (0.0009)
+[2023-09-11 21:43:31,384][88175] Fps is (10 sec: 11468.7, 60 sec: 12219.7, 300 sec: 12690.7). Total num frames: 20926464. Throughput: 0: 2882.3. Samples: 4228404. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:43:31,385][88175] Avg episode reward: [(0, '9.880')]
+[2023-09-11 21:43:31,440][83013] Updated weights for policy 0, policy_version 5110 (0.0008)
+[2023-09-11 21:43:35,003][83013] Updated weights for policy 0, policy_version 5120 (0.0008)
+[2023-09-11 21:43:36,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11810.1, 300 sec: 12690.7). Total num frames: 20983808. Throughput: 0: 2882.1. Samples: 4236986. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:43:36,386][88175] Avg episode reward: [(0, '9.660')]
+[2023-09-11 21:43:38,585][83013] Updated weights for policy 0, policy_version 5130 (0.0008)
+[2023-09-11 21:43:41,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12649.0). Total num frames: 21041152. Throughput: 0: 2880.7. Samples: 4254228. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:43:41,385][88175] Avg episode reward: [(0, '9.650')]
+[2023-09-11 21:43:42,217][83013] Updated weights for policy 0, policy_version 5140 (0.0010)
+[2023-09-11 21:43:45,662][83013] Updated weights for policy 0, policy_version 5150 (0.0009)
+[2023-09-11 21:43:46,383][88175] Fps is (10 sec: 11878.6, 60 sec: 11537.1, 300 sec: 12579.6). Total num frames: 21102592. Throughput: 0: 2882.9. Samples: 4271518. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:43:46,385][88175] Avg episode reward: [(0, '9.690')]
+[2023-09-11 21:43:49,192][83013] Updated weights for policy 0, policy_version 5160 (0.0008)
+[2023-09-11 21:43:51,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11537.1, 300 sec: 12496.3). Total num frames: 21159936. Throughput: 0: 2881.7. Samples: 4280070. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:43:51,385][88175] Avg episode reward: [(0, '9.790')]
+[2023-09-11 21:43:52,746][83013] Updated weights for policy 0, policy_version 5170 (0.0008)
+[2023-09-11 21:43:56,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12413.0). Total num frames: 21217280. Throughput: 0: 2873.7. Samples: 4297412. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:43:56,385][83013] Updated weights for policy 0, policy_version 5180 (0.0009)
+[2023-09-11 21:43:56,385][88175] Avg episode reward: [(0, '9.580')]
+[2023-09-11 21:43:59,967][83013] Updated weights for policy 0, policy_version 5190 (0.0010)
+[2023-09-11 21:44:01,383][88175] Fps is (10 sec: 11059.2, 60 sec: 11468.8, 300 sec: 12301.9). Total num frames: 21270528. Throughput: 0: 2873.1. Samples: 4314652. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:44:01,385][88175] Avg episode reward: [(0, '9.570')]
+[2023-09-11 21:44:03,470][83013] Updated weights for policy 0, policy_version 5200 (0.0008)
+[2023-09-11 21:44:06,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11537.1, 300 sec: 12274.1). Total num frames: 21331968. Throughput: 0: 2872.6. Samples: 4323192. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:44:06,385][88175] Avg episode reward: [(0, '9.760')]
+[2023-09-11 21:44:06,394][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000005208_21331968.pth...
+[2023-09-11 21:44:06,447][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000004479_18345984.pth
+[2023-09-11 21:44:07,098][83013] Updated weights for policy 0, policy_version 5210 (0.0008)
+[2023-09-11 21:44:10,613][83013] Updated weights for policy 0, policy_version 5220 (0.0009)
+[2023-09-11 21:44:11,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11537.1, 300 sec: 12274.1). Total num frames: 21389312. Throughput: 0: 2874.0. Samples: 4340446. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:44:11,385][88175] Avg episode reward: [(0, '9.840')]
+[2023-09-11 21:44:14,196][83013] Updated weights for policy 0, policy_version 5230 (0.0008)
+[2023-09-11 21:44:16,383][88175] Fps is (10 sec: 11469.2, 60 sec: 11537.1, 300 sec: 12274.1). Total num frames: 21446656. Throughput: 0: 2874.7. Samples: 4357764. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:44:16,384][88175] Avg episode reward: [(0, '9.660')]
+[2023-09-11 21:44:17,740][83013] Updated weights for policy 0, policy_version 5240 (0.0009)
+[2023-09-11 21:44:21,320][83013] Updated weights for policy 0, policy_version 5250 (0.0010)
+[2023-09-11 21:44:21,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12274.1). Total num frames: 21504000. Throughput: 0: 2873.3. Samples: 4366286. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:44:21,384][88175] Avg episode reward: [(0, '9.750')]
+[2023-09-11 21:44:24,977][83013] Updated weights for policy 0, policy_version 5260 (0.0009)
+[2023-09-11 21:44:26,384][88175] Fps is (10 sec: 11058.9, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 21557248. Throughput: 0: 2871.1. Samples: 4383428. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:44:26,385][88175] Avg episode reward: [(0, '10.040')]
+[2023-09-11 21:44:28,484][83013] Updated weights for policy 0, policy_version 5270 (0.0008)
+[2023-09-11 21:44:31,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.1, 300 sec: 12274.1). Total num frames: 21618688. Throughput: 0: 2869.2. Samples: 4400630. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:44:31,385][88175] Avg episode reward: [(0, '9.740')]
+[2023-09-11 21:44:32,071][83013] Updated weights for policy 0, policy_version 5280 (0.0009)
+[2023-09-11 21:44:35,723][83013] Updated weights for policy 0, policy_version 5290 (0.0008)
+[2023-09-11 21:44:36,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 21671936. Throughput: 0: 2867.2. Samples: 4409096. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:44:36,385][88175] Avg episode reward: [(0, '9.810')]
+[2023-09-11 21:44:39,301][83013] Updated weights for policy 0, policy_version 5300 (0.0008)
+[2023-09-11 21:44:41,384][88175] Fps is (10 sec: 11059.0, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 21729280. Throughput: 0: 2862.6. Samples: 4426230. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:44:41,385][88175] Avg episode reward: [(0, '10.150')]
+[2023-09-11 21:44:42,735][83013] Updated weights for policy 0, policy_version 5310 (0.0008)
+[2023-09-11 21:44:46,312][83013] Updated weights for policy 0, policy_version 5320 (0.0010)
+[2023-09-11 21:44:46,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 21790720. Throughput: 0: 2868.8. Samples: 4443748. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:44:46,384][88175] Avg episode reward: [(0, '9.950')]
+[2023-09-11 21:44:49,896][83013] Updated weights for policy 0, policy_version 5330 (0.0009)
+[2023-09-11 21:44:51,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 21848064. Throughput: 0: 2866.9. Samples: 4452200. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:44:51,385][88175] Avg episode reward: [(0, '9.910')]
+[2023-09-11 21:44:53,414][83013] Updated weights for policy 0, policy_version 5340 (0.0009)
+[2023-09-11 21:44:56,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 21905408. Throughput: 0: 2871.5. Samples: 4469664. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:44:56,385][88175] Avg episode reward: [(0, '9.840')]
+[2023-09-11 21:44:57,057][83013] Updated weights for policy 0, policy_version 5350 (0.0008)
+[2023-09-11 21:45:00,667][83013] Updated weights for policy 0, policy_version 5360 (0.0008)
+[2023-09-11 21:45:01,384][88175] Fps is (10 sec: 11059.1, 60 sec: 11468.8, 300 sec: 12246.4). Total num frames: 21958656. Throughput: 0: 2864.0. Samples: 4486646. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:45:01,385][88175] Avg episode reward: [(0, '9.980')]
+[2023-09-11 21:45:04,269][83013] Updated weights for policy 0, policy_version 5370 (0.0009)
+[2023-09-11 21:45:06,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.9, 300 sec: 12260.2). Total num frames: 22020096. Throughput: 0: 2865.2. Samples: 4495218. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
+[2023-09-11 21:45:06,384][88175] Avg episode reward: [(0, '9.760')]
+[2023-09-11 21:45:07,699][83013] Updated weights for policy 0, policy_version 5380 (0.0008)
+[2023-09-11 21:45:11,256][83013] Updated weights for policy 0, policy_version 5390 (0.0009)
+[2023-09-11 21:45:11,383][88175] Fps is (10 sec: 11878.6, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 22077440. Throughput: 0: 2870.7. Samples: 4512608. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:45:11,385][88175] Avg episode reward: [(0, '9.730')]
+[2023-09-11 21:45:14,839][83013] Updated weights for policy 0, policy_version 5400 (0.0009)
+[2023-09-11 21:45:16,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 22134784. Throughput: 0: 2870.1. Samples: 4529784. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:45:16,385][88175] Avg episode reward: [(0, '10.000')]
+[2023-09-11 21:45:18,355][83013] Updated weights for policy 0, policy_version 5410 (0.0008)
+[2023-09-11 21:45:21,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 22192128. Throughput: 0: 2875.6. Samples: 4538500. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:45:21,385][88175] Avg episode reward: [(0, '9.880')]
+[2023-09-11 21:45:21,931][83013] Updated weights for policy 0, policy_version 5420 (0.0008)
+[2023-09-11 21:45:25,591][83013] Updated weights for policy 0, policy_version 5430 (0.0009)
+[2023-09-11 21:45:26,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.1, 300 sec: 12260.2). Total num frames: 22249472. Throughput: 0: 2872.8. Samples: 4555506. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:45:26,385][88175] Avg episode reward: [(0, '9.830')]
+[2023-09-11 21:45:28,339][83013] Updated weights for policy 0, policy_version 5440 (0.0009)
+[2023-09-11 21:45:30,869][83013] Updated weights for policy 0, policy_version 5450 (0.0009)
+[2023-09-11 21:45:31,383][88175] Fps is (10 sec: 13516.8, 60 sec: 11810.1, 300 sec: 12329.7). Total num frames: 22327296. Throughput: 0: 2982.9. Samples: 4577980. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:45:31,385][88175] Avg episode reward: [(0, '9.940')]
+[2023-09-11 21:45:33,392][83013] Updated weights for policy 0, policy_version 5460 (0.0009)
+[2023-09-11 21:45:35,853][83013] Updated weights for policy 0, policy_version 5470 (0.0008)
+[2023-09-11 21:45:36,383][88175] Fps is (10 sec: 16383.9, 60 sec: 12356.3, 300 sec: 12426.8). Total num frames: 22413312. Throughput: 0: 3068.9. Samples: 4590300. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:45:36,385][88175] Avg episode reward: [(0, '9.850')]
+[2023-09-11 21:45:38,387][83013] Updated weights for policy 0, policy_version 5480 (0.0009)
+[2023-09-11 21:45:40,852][83013] Updated weights for policy 0, policy_version 5490 (0.0008)
+[2023-09-11 21:45:41,383][88175] Fps is (10 sec: 16383.9, 60 sec: 12697.6, 300 sec: 12496.3). Total num frames: 22491136. Throughput: 0: 3229.3. Samples: 4614984. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:45:41,385][88175] Avg episode reward: [(0, '9.800')]
+[2023-09-11 21:45:43,364][83013] Updated weights for policy 0, policy_version 5500 (0.0009)
+[2023-09-11 21:45:45,859][83013] Updated weights for policy 0, policy_version 5510 (0.0009)
+[2023-09-11 21:45:46,383][88175] Fps is (10 sec: 16384.3, 60 sec: 13107.2, 300 sec: 12593.5). Total num frames: 22577152. Throughput: 0: 3395.5. Samples: 4639444. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:45:46,384][88175] Avg episode reward: [(0, '9.650')]
+[2023-09-11 21:45:48,326][83013] Updated weights for policy 0, policy_version 5520 (0.0009)
+[2023-09-11 21:45:50,803][83013] Updated weights for policy 0, policy_version 5530 (0.0009)
+[2023-09-11 21:45:51,383][88175] Fps is (10 sec: 16793.7, 60 sec: 13516.8, 300 sec: 12676.8). Total num frames: 22659072. Throughput: 0: 3485.5. Samples: 4652064. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 21:45:51,385][88175] Avg episode reward: [(0, '9.860')]
+[2023-09-11 21:45:53,312][83013] Updated weights for policy 0, policy_version 5540 (0.0009)
+[2023-09-11 21:45:55,802][83013] Updated weights for policy 0, policy_version 5550 (0.0008)
+[2023-09-11 21:45:56,383][88175] Fps is (10 sec: 16383.7, 60 sec: 13926.4, 300 sec: 12760.1). Total num frames: 22740992. Throughput: 0: 3647.6. Samples: 4676750. Policy #0 lag: (min: 0.0, avg: 0.8, max: 1.0)
+[2023-09-11 21:45:56,385][88175] Avg episode reward: [(0, '9.780')]
+[2023-09-11 21:45:58,300][83013] Updated weights for policy 0, policy_version 5560 (0.0008)
+[2023-09-11 21:46:00,759][83013] Updated weights for policy 0, policy_version 5570 (0.0009)
+[2023-09-11 21:46:01,383][88175] Fps is (10 sec: 16384.1, 60 sec: 14404.3, 300 sec: 12843.4). Total num frames: 22822912. Throughput: 0: 3816.2. Samples: 4701512. Policy #0 lag: (min: 0.0, avg: 0.8, max: 1.0)
+[2023-09-11 21:46:01,387][88175] Avg episode reward: [(0, '9.840')]
+[2023-09-11 21:46:03,243][83013] Updated weights for policy 0, policy_version 5580 (0.0008)
+[2023-09-11 21:46:06,272][83013] Updated weights for policy 0, policy_version 5590 (0.0008)
+[2023-09-11 21:46:06,384][88175] Fps is (10 sec: 15564.6, 60 sec: 14609.0, 300 sec: 12898.9). Total num frames: 22896640. Throughput: 0: 3903.0. Samples: 4714134. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:46:06,385][88175] Avg episode reward: [(0, '9.780')]
+[2023-09-11 21:46:06,392][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000005590_22896640.pth...
+[2023-09-11 21:46:06,441][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000004829_19779584.pth
+[2023-09-11 21:46:09,785][83013] Updated weights for policy 0, policy_version 5600 (0.0009)
+[2023-09-11 21:46:11,383][88175] Fps is (10 sec: 13107.1, 60 sec: 14609.1, 300 sec: 12898.9). Total num frames: 22953984. Throughput: 0: 3917.6. Samples: 4731796. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:46:11,385][88175] Avg episode reward: [(0, '9.990')]
+[2023-09-11 21:46:13,355][83013] Updated weights for policy 0, policy_version 5610 (0.0009)
+[2023-09-11 21:46:16,383][88175] Fps is (10 sec: 11469.1, 60 sec: 14609.1, 300 sec: 12898.9). Total num frames: 23011328. Throughput: 0: 3799.4. Samples: 4748952. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:46:16,385][88175] Avg episode reward: [(0, '10.050')]
+[2023-09-11 21:46:16,982][83013] Updated weights for policy 0, policy_version 5620 (0.0008)
+[2023-09-11 21:46:20,563][83013] Updated weights for policy 0, policy_version 5630 (0.0008)
+[2023-09-11 21:46:21,383][88175] Fps is (10 sec: 11468.8, 60 sec: 14609.1, 300 sec: 12898.9). Total num frames: 23068672. Throughput: 0: 3717.3. Samples: 4757580. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:46:21,384][88175] Avg episode reward: [(0, '9.850')]
+[2023-09-11 21:46:24,180][83013] Updated weights for policy 0, policy_version 5640 (0.0008)
+[2023-09-11 21:46:26,383][88175] Fps is (10 sec: 11468.8, 60 sec: 14609.1, 300 sec: 12898.9). Total num frames: 23126016. Throughput: 0: 3545.5. Samples: 4774530. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:46:26,385][88175] Avg episode reward: [(0, '10.120')]
+[2023-09-11 21:46:27,736][83013] Updated weights for policy 0, policy_version 5650 (0.0008)
+[2023-09-11 21:46:31,293][83013] Updated weights for policy 0, policy_version 5660 (0.0008)
+[2023-09-11 21:46:31,384][88175] Fps is (10 sec: 11468.5, 60 sec: 14267.7, 300 sec: 12898.9). Total num frames: 23183360. Throughput: 0: 3388.7. Samples: 4791936. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:46:31,385][88175] Avg episode reward: [(0, '9.920')]
+[2023-09-11 21:46:34,909][83013] Updated weights for policy 0, policy_version 5670 (0.0008)
+[2023-09-11 21:46:36,383][88175] Fps is (10 sec: 11468.7, 60 sec: 13789.9, 300 sec: 12898.9). Total num frames: 23240704. Throughput: 0: 3295.3. Samples: 4800354. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:46:36,385][88175] Avg episode reward: [(0, '9.900')]
+[2023-09-11 21:46:38,435][83013] Updated weights for policy 0, policy_version 5680 (0.0008)
+[2023-09-11 21:46:41,383][88175] Fps is (10 sec: 11469.0, 60 sec: 13448.5, 300 sec: 12898.9). Total num frames: 23298048. Throughput: 0: 3133.0. Samples: 4817736. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:46:41,385][88175] Avg episode reward: [(0, '9.850')]
+[2023-09-11 21:46:41,998][83013] Updated weights for policy 0, policy_version 5690 (0.0009)
+[2023-09-11 21:46:45,580][83013] Updated weights for policy 0, policy_version 5700 (0.0008)
+[2023-09-11 21:46:46,383][88175] Fps is (10 sec: 11468.8, 60 sec: 12970.7, 300 sec: 12898.9). Total num frames: 23355392. Throughput: 0: 2964.5. Samples: 4834916. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:46:46,385][88175] Avg episode reward: [(0, '10.120')]
+[2023-09-11 21:46:49,221][83013] Updated weights for policy 0, policy_version 5710 (0.0009)
+[2023-09-11 21:46:51,383][88175] Fps is (10 sec: 11059.2, 60 sec: 12492.8, 300 sec: 12857.3). Total num frames: 23408640. Throughput: 0: 2869.7. Samples: 4843272. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:46:51,385][88175] Avg episode reward: [(0, '10.040')]
+[2023-09-11 21:46:52,790][83013] Updated weights for policy 0, policy_version 5720 (0.0008)
+[2023-09-11 21:46:56,330][83013] Updated weights for policy 0, policy_version 5730 (0.0008)
+[2023-09-11 21:46:56,383][88175] Fps is (10 sec: 11468.8, 60 sec: 12151.5, 300 sec: 12787.9). Total num frames: 23470080. Throughput: 0: 2861.1. Samples: 4860544. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:46:56,385][88175] Avg episode reward: [(0, '9.830')]
+[2023-09-11 21:46:59,985][83013] Updated weights for policy 0, policy_version 5740 (0.0008)
+[2023-09-11 21:47:01,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11673.6, 300 sec: 12690.7). Total num frames: 23523328. Throughput: 0: 2861.2. Samples: 4877706. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:47:01,385][88175] Avg episode reward: [(0, '9.720')]
+[2023-09-11 21:47:03,428][83013] Updated weights for policy 0, policy_version 5750 (0.0009)
+[2023-09-11 21:47:06,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11468.8, 300 sec: 12621.2). Total num frames: 23584768. Throughput: 0: 2860.8. Samples: 4886316. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:47:06,385][88175] Avg episode reward: [(0, '9.900')]
+[2023-09-11 21:47:07,107][83013] Updated weights for policy 0, policy_version 5760 (0.0009)
+[2023-09-11 21:47:10,724][83013] Updated weights for policy 0, policy_version 5770 (0.0008)
+[2023-09-11 21:47:11,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11468.8, 300 sec: 12537.9). Total num frames: 23642112. Throughput: 0: 2865.3. Samples: 4903470. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:47:11,385][88175] Avg episode reward: [(0, '9.890')]
+[2023-09-11 21:47:14,261][83013] Updated weights for policy 0, policy_version 5780 (0.0010)
+[2023-09-11 21:47:16,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12440.7). Total num frames: 23699456. Throughput: 0: 2863.9. Samples: 4920810. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:47:16,385][88175] Avg episode reward: [(0, '9.600')]
+[2023-09-11 21:47:17,807][83013] Updated weights for policy 0, policy_version 5790 (0.0009)
+[2023-09-11 21:47:21,344][83013] Updated weights for policy 0, policy_version 5800 (0.0008)
+[2023-09-11 21:47:21,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12357.4). Total num frames: 23756800. Throughput: 0: 2865.6. Samples: 4929308. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:47:21,385][88175] Avg episode reward: [(0, '10.030')]
+[2023-09-11 21:47:24,893][83013] Updated weights for policy 0, policy_version 5810 (0.0008)
+[2023-09-11 21:47:26,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12274.1). Total num frames: 23814144. Throughput: 0: 2861.9. Samples: 4946522. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:47:26,385][88175] Avg episode reward: [(0, '9.920')]
+[2023-09-11 21:47:28,501][83013] Updated weights for policy 0, policy_version 5820 (0.0008)
+[2023-09-11 21:47:31,383][88175] Fps is (10 sec: 11059.3, 60 sec: 11400.6, 300 sec: 12176.9). Total num frames: 23867392. Throughput: 0: 2860.4. Samples: 4963632. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:47:31,384][88175] Avg episode reward: [(0, '10.100')]
+[2023-09-11 21:47:32,117][83013] Updated weights for policy 0, policy_version 5830 (0.0008)
+[2023-09-11 21:47:35,638][83013] Updated weights for policy 0, policy_version 5840 (0.0009)
+[2023-09-11 21:47:36,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12121.4). Total num frames: 23928832. Throughput: 0: 2866.4. Samples: 4972262. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:47:36,385][88175] Avg episode reward: [(0, '10.040')]
+[2023-09-11 21:47:39,284][83013] Updated weights for policy 0, policy_version 5850 (0.0009)
+[2023-09-11 21:47:41,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11400.5, 300 sec: 12107.5). Total num frames: 23982080. Throughput: 0: 2863.6. Samples: 4989406. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:47:41,385][88175] Avg episode reward: [(0, '9.750')]
+[2023-09-11 21:47:42,897][83013] Updated weights for policy 0, policy_version 5860 (0.0008)
+[2023-09-11 21:47:46,384][88175] Fps is (10 sec: 11059.0, 60 sec: 11400.5, 300 sec: 12107.5). Total num frames: 24039424. Throughput: 0: 2864.4. Samples: 5006604. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:47:46,385][88175] Avg episode reward: [(0, '9.870')]
+[2023-09-11 21:47:46,401][83013] Updated weights for policy 0, policy_version 5870 (0.0009)
+[2023-09-11 21:47:49,984][83013] Updated weights for policy 0, policy_version 5880 (0.0012)
+[2023-09-11 21:47:51,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 24096768. Throughput: 0: 2865.4. Samples: 5015258. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:47:51,385][88175] Avg episode reward: [(0, '10.130')]
+[2023-09-11 21:47:53,586][83013] Updated weights for policy 0, policy_version 5890 (0.0008)
+[2023-09-11 21:47:56,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11400.5, 300 sec: 12107.5). Total num frames: 24154112. Throughput: 0: 2865.2. Samples: 5032404. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:47:56,385][88175] Avg episode reward: [(0, '10.040')]
+[2023-09-11 21:47:57,115][83013] Updated weights for policy 0, policy_version 5900 (0.0008)
+[2023-09-11 21:48:00,720][83013] Updated weights for policy 0, policy_version 5910 (0.0008)
+[2023-09-11 21:48:01,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 24211456. Throughput: 0: 2856.9. Samples: 5049368. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:48:01,385][88175] Avg episode reward: [(0, '9.850')]
+[2023-09-11 21:48:04,352][83013] Updated weights for policy 0, policy_version 5920 (0.0008)
+[2023-09-11 21:48:06,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11400.6, 300 sec: 12107.5). Total num frames: 24268800. Throughput: 0: 2859.2. Samples: 5057970. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:48:06,385][88175] Avg episode reward: [(0, '9.860')]
+[2023-09-11 21:48:06,391][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000005925_24268800.pth...
+[2023-09-11 21:48:06,441][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000005208_21331968.pth
+[2023-09-11 21:48:07,854][83013] Updated weights for policy 0, policy_version 5930 (0.0008)
+[2023-09-11 21:48:11,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11400.5, 300 sec: 12107.5). Total num frames: 24326144. Throughput: 0: 2862.9. Samples: 5075350. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:48:11,385][88175] Avg episode reward: [(0, '9.850')]
+[2023-09-11 21:48:11,407][83013] Updated weights for policy 0, policy_version 5940 (0.0009)
+[2023-09-11 21:48:14,992][83013] Updated weights for policy 0, policy_version 5950 (0.0009)
+[2023-09-11 21:48:16,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11400.5, 300 sec: 12107.5). Total num frames: 24383488. Throughput: 0: 2864.0. Samples: 5092512. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:48:16,385][88175] Avg episode reward: [(0, '10.100')]
+[2023-09-11 21:48:18,541][83013] Updated weights for policy 0, policy_version 5960 (0.0008)
+[2023-09-11 21:48:21,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11400.5, 300 sec: 12107.5). Total num frames: 24440832. Throughput: 0: 2865.0. Samples: 5101186. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:48:21,385][88175] Avg episode reward: [(0, '9.860')]
+[2023-09-11 21:48:22,107][83013] Updated weights for policy 0, policy_version 5970 (0.0008)
+[2023-09-11 21:48:25,712][83013] Updated weights for policy 0, policy_version 5980 (0.0009)
+[2023-09-11 21:48:26,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11400.6, 300 sec: 12107.5). Total num frames: 24498176. Throughput: 0: 2863.4. Samples: 5118258. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:48:26,384][88175] Avg episode reward: [(0, '9.760')]
+[2023-09-11 21:48:29,320][83013] Updated weights for policy 0, policy_version 5990 (0.0009)
+[2023-09-11 21:48:31,384][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 24555520. Throughput: 0: 2862.6. Samples: 5135422. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:48:31,385][88175] Avg episode reward: [(0, '9.660')]
+[2023-09-11 21:48:32,838][83013] Updated weights for policy 0, policy_version 6000 (0.0009)
+[2023-09-11 21:48:36,378][83013] Updated weights for policy 0, policy_version 6010 (0.0008)
+[2023-09-11 21:48:36,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11468.8, 300 sec: 12121.4). Total num frames: 24616960. Throughput: 0: 2863.4. Samples: 5144112. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:48:36,385][88175] Avg episode reward: [(0, '9.820')]
+[2023-09-11 21:48:40,035][83013] Updated weights for policy 0, policy_version 6020 (0.0009)
+[2023-09-11 21:48:41,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12093.6). Total num frames: 24670208. Throughput: 0: 2866.0. Samples: 5161374. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:48:41,385][88175] Avg episode reward: [(0, '9.880')]
+[2023-09-11 21:48:43,556][83013] Updated weights for policy 0, policy_version 6030 (0.0008)
+[2023-09-11 21:48:46,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12107.5). Total num frames: 24731648. Throughput: 0: 2875.0. Samples: 5178744. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:48:46,385][88175] Avg episode reward: [(0, '9.800')]
+[2023-09-11 21:48:47,073][83013] Updated weights for policy 0, policy_version 6040 (0.0009)
+[2023-09-11 21:48:50,517][83013] Updated weights for policy 0, policy_version 6050 (0.0008)
+[2023-09-11 21:48:51,383][88175] Fps is (10 sec: 12288.0, 60 sec: 11605.3, 300 sec: 12121.4). Total num frames: 24793088. Throughput: 0: 2877.6. Samples: 5187460. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:48:51,384][88175] Avg episode reward: [(0, '9.930')]
+[2023-09-11 21:48:52,997][83013] Updated weights for policy 0, policy_version 6060 (0.0009)
+[2023-09-11 21:48:55,456][83013] Updated weights for policy 0, policy_version 6070 (0.0008)
+[2023-09-11 21:48:56,383][88175] Fps is (10 sec: 14336.3, 60 sec: 12015.0, 300 sec: 12218.6). Total num frames: 24875008. Throughput: 0: 3006.9. Samples: 5210658. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:48:56,385][88175] Avg episode reward: [(0, '10.090')]
+[2023-09-11 21:48:57,944][83013] Updated weights for policy 0, policy_version 6080 (0.0008)
+[2023-09-11 21:49:00,448][83013] Updated weights for policy 0, policy_version 6090 (0.0008)
+[2023-09-11 21:49:01,383][88175] Fps is (10 sec: 16384.1, 60 sec: 12424.6, 300 sec: 12288.0). Total num frames: 24956928. Throughput: 0: 3174.8. Samples: 5235376. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:49:01,384][88175] Avg episode reward: [(0, '9.920')]
+[2023-09-11 21:49:02,932][83013] Updated weights for policy 0, policy_version 6100 (0.0008)
+[2023-09-11 21:49:05,405][83013] Updated weights for policy 0, policy_version 6110 (0.0009)
+[2023-09-11 21:49:06,383][88175] Fps is (10 sec: 16793.5, 60 sec: 12902.4, 300 sec: 12385.2). Total num frames: 25042944. Throughput: 0: 3256.4. Samples: 5247722. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:49:06,385][88175] Avg episode reward: [(0, '10.140')]
+[2023-09-11 21:49:07,856][83013] Updated weights for policy 0, policy_version 6120 (0.0008)
+[2023-09-11 21:49:10,299][83013] Updated weights for policy 0, policy_version 6130 (0.0008)
+[2023-09-11 21:49:11,383][88175] Fps is (10 sec: 16793.5, 60 sec: 13312.0, 300 sec: 12468.5). Total num frames: 25124864. Throughput: 0: 3428.9. Samples: 5272560. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:49:11,384][88175] Avg episode reward: [(0, '9.960')]
+[2023-09-11 21:49:12,735][83013] Updated weights for policy 0, policy_version 6140 (0.0008)
+[2023-09-11 21:49:15,227][83013] Updated weights for policy 0, policy_version 6150 (0.0008)
+[2023-09-11 21:49:16,383][88175] Fps is (10 sec: 16383.9, 60 sec: 13721.6, 300 sec: 12551.8). Total num frames: 25206784. Throughput: 0: 3603.2. Samples: 5297568. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:49:16,385][88175] Avg episode reward: [(0, '10.160')]
+[2023-09-11 21:49:17,674][83013] Updated weights for policy 0, policy_version 6160 (0.0008)
+[2023-09-11 21:49:20,152][83013] Updated weights for policy 0, policy_version 6170 (0.0008)
+[2023-09-11 21:49:21,383][88175] Fps is (10 sec: 16793.5, 60 sec: 14199.5, 300 sec: 12662.9). Total num frames: 25292800. Throughput: 0: 3685.0. Samples: 5309936. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:49:21,385][88175] Avg episode reward: [(0, '10.220')]
+[2023-09-11 21:49:22,642][83013] Updated weights for policy 0, policy_version 6180 (0.0008)
+[2023-09-11 21:49:25,081][83013] Updated weights for policy 0, policy_version 6190 (0.0008)
+[2023-09-11 21:49:26,384][88175] Fps is (10 sec: 16383.8, 60 sec: 14540.8, 300 sec: 12718.4). Total num frames: 25370624. Throughput: 0: 3859.4. Samples: 5335050. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:49:26,385][88175] Avg episode reward: [(0, '9.780')]
+[2023-09-11 21:49:28,367][83013] Updated weights for policy 0, policy_version 6200 (0.0008)
+[2023-09-11 21:49:31,383][88175] Fps is (10 sec: 13516.9, 60 sec: 14540.8, 300 sec: 12732.3). Total num frames: 25427968. Throughput: 0: 3876.4. Samples: 5353180. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:49:31,384][88175] Avg episode reward: [(0, '9.890')]
+[2023-09-11 21:49:31,959][83013] Updated weights for policy 0, policy_version 6210 (0.0008)
+[2023-09-11 21:49:35,545][83013] Updated weights for policy 0, policy_version 6220 (0.0008)
+[2023-09-11 21:49:36,383][88175] Fps is (10 sec: 11469.1, 60 sec: 14472.6, 300 sec: 12732.3). Total num frames: 25485312. Throughput: 0: 3875.4. Samples: 5361852. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:49:36,384][88175] Avg episode reward: [(0, '9.950')]
+[2023-09-11 21:49:39,072][83013] Updated weights for policy 0, policy_version 6230 (0.0009)
+[2023-09-11 21:49:41,383][88175] Fps is (10 sec: 11468.8, 60 sec: 14540.8, 300 sec: 12718.4). Total num frames: 25542656. Throughput: 0: 3744.6. Samples: 5379166. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:49:41,384][88175] Avg episode reward: [(0, '10.080')]
+[2023-09-11 21:49:42,639][83013] Updated weights for policy 0, policy_version 6240 (0.0008)
+[2023-09-11 21:49:46,163][83013] Updated weights for policy 0, policy_version 6250 (0.0009)
+[2023-09-11 21:49:46,383][88175] Fps is (10 sec: 11468.8, 60 sec: 14472.6, 300 sec: 12718.4). Total num frames: 25600000. Throughput: 0: 3578.0. Samples: 5396386. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:49:46,384][88175] Avg episode reward: [(0, '9.990')]
+[2023-09-11 21:49:49,695][83013] Updated weights for policy 0, policy_version 6260 (0.0008)
+[2023-09-11 21:49:51,383][88175] Fps is (10 sec: 11468.7, 60 sec: 14404.3, 300 sec: 12718.4). Total num frames: 25657344. Throughput: 0: 3500.2. Samples: 5405230. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:49:51,384][88175] Avg episode reward: [(0, '9.840')]
+[2023-09-11 21:49:53,328][83013] Updated weights for policy 0, policy_version 6270 (0.0009)
+[2023-09-11 21:49:56,383][88175] Fps is (10 sec: 11468.9, 60 sec: 13994.7, 300 sec: 12732.3). Total num frames: 25714688. Throughput: 0: 3325.2. Samples: 5422196. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:49:56,385][88175] Avg episode reward: [(0, '9.870')]
+[2023-09-11 21:49:56,950][83013] Updated weights for policy 0, policy_version 6280 (0.0008)
+[2023-09-11 21:50:00,483][83013] Updated weights for policy 0, policy_version 6290 (0.0009)
+[2023-09-11 21:50:01,384][88175] Fps is (10 sec: 11468.4, 60 sec: 13585.0, 300 sec: 12718.4). Total num frames: 25772032. Throughput: 0: 3147.8. Samples: 5439218. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:50:01,386][88175] Avg episode reward: [(0, '10.200')]
+[2023-09-11 21:50:04,113][83013] Updated weights for policy 0, policy_version 6300 (0.0009)
+[2023-09-11 21:50:06,383][88175] Fps is (10 sec: 11468.6, 60 sec: 13107.2, 300 sec: 12718.4). Total num frames: 25829376. Throughput: 0: 3064.4. Samples: 5447834. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:50:06,385][88175] Avg episode reward: [(0, '9.970')]
+[2023-09-11 21:50:06,393][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000006306_25829376.pth...
+[2023-09-11 21:50:06,442][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000005590_22896640.pth
+[2023-09-11 21:50:07,686][83013] Updated weights for policy 0, policy_version 6310 (0.0008)
+[2023-09-11 21:50:11,227][83013] Updated weights for policy 0, policy_version 6320 (0.0009)
+[2023-09-11 21:50:11,383][88175] Fps is (10 sec: 11469.2, 60 sec: 12697.6, 300 sec: 12718.4). Total num frames: 25886720. Throughput: 0: 2889.8. Samples: 5465092. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:50:11,384][88175] Avg episode reward: [(0, '9.850')]
+[2023-09-11 21:50:14,829][83013] Updated weights for policy 0, policy_version 6330 (0.0008)
+[2023-09-11 21:50:16,383][88175] Fps is (10 sec: 11468.9, 60 sec: 12288.0, 300 sec: 12718.4). Total num frames: 25944064. Throughput: 0: 2867.9. Samples: 5482236. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:50:16,384][88175] Avg episode reward: [(0, '10.110')]
+[2023-09-11 21:50:18,340][83013] Updated weights for policy 0, policy_version 6340 (0.0008)
+[2023-09-11 21:50:21,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11810.1, 300 sec: 12718.4). Total num frames: 26001408. Throughput: 0: 2870.4. Samples: 5491020. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:50:21,385][88175] Avg episode reward: [(0, '10.230')]
+[2023-09-11 21:50:21,880][83013] Updated weights for policy 0, policy_version 6350 (0.0008)
+[2023-09-11 21:50:25,443][83013] Updated weights for policy 0, policy_version 6360 (0.0008)
+[2023-09-11 21:50:26,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12649.0). Total num frames: 26058752. Throughput: 0: 2867.4. Samples: 5508200. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:50:26,385][88175] Avg episode reward: [(0, '10.000')]
+[2023-09-11 21:50:29,038][83013] Updated weights for policy 0, policy_version 6370 (0.0008)
+[2023-09-11 21:50:31,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12551.8). Total num frames: 26116096. Throughput: 0: 2870.7. Samples: 5525566. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:50:31,385][88175] Avg episode reward: [(0, '9.850')]
+[2023-09-11 21:50:32,540][83013] Updated weights for policy 0, policy_version 6380 (0.0008)
+[2023-09-11 21:50:36,232][83013] Updated weights for policy 0, policy_version 6390 (0.0008)
+[2023-09-11 21:50:36,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12482.4). Total num frames: 26173440. Throughput: 0: 2865.6. Samples: 5534180. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:50:36,385][88175] Avg episode reward: [(0, '10.150')]
+[2023-09-11 21:50:39,802][83013] Updated weights for policy 0, policy_version 6400 (0.0009)
+[2023-09-11 21:50:41,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12385.2). Total num frames: 26230784. Throughput: 0: 2867.3. Samples: 5551226. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:50:41,385][88175] Avg episode reward: [(0, '10.220')]
+[2023-09-11 21:50:43,366][83013] Updated weights for policy 0, policy_version 6410 (0.0009)
+[2023-09-11 21:50:46,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12301.9). Total num frames: 26288128. Throughput: 0: 2872.1. Samples: 5568460. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:50:46,384][88175] Avg episode reward: [(0, '9.990')]
+[2023-09-11 21:50:46,863][83013] Updated weights for policy 0, policy_version 6420 (0.0009)
+[2023-09-11 21:50:50,370][83013] Updated weights for policy 0, policy_version 6430 (0.0009)
+[2023-09-11 21:50:51,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 12218.6). Total num frames: 26345472. Throughput: 0: 2877.3. Samples: 5577312. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:50:51,385][88175] Avg episode reward: [(0, '9.970')]
+[2023-09-11 21:50:53,963][83013] Updated weights for policy 0, policy_version 6440 (0.0008)
+[2023-09-11 21:50:56,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 26402816. Throughput: 0: 2875.8. Samples: 5594504. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:50:56,385][88175] Avg episode reward: [(0, '10.020')]
+[2023-09-11 21:50:57,565][83013] Updated weights for policy 0, policy_version 6450 (0.0009)
+[2023-09-11 21:51:01,016][83013] Updated weights for policy 0, policy_version 6460 (0.0008)
+[2023-09-11 21:51:01,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.9, 300 sec: 12079.7). Total num frames: 26460160. Throughput: 0: 2882.8. Samples: 5611962. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 21:51:01,385][88175] Avg episode reward: [(0, '10.100')]
+[2023-09-11 21:51:04,622][83013] Updated weights for policy 0, policy_version 6470 (0.0010)
+[2023-09-11 21:51:06,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12079.7). Total num frames: 26517504. Throughput: 0: 2876.5. Samples: 5620464. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:51:06,385][88175] Avg episode reward: [(0, '10.220')]
+[2023-09-11 21:51:08,160][83013] Updated weights for policy 0, policy_version 6480 (0.0009)
+[2023-09-11 21:51:11,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11537.1, 300 sec: 12093.6). Total num frames: 26578944. Throughput: 0: 2879.9. Samples: 5637794. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:51:11,385][88175] Avg episode reward: [(0, '10.020')]
+[2023-09-11 21:51:11,703][83013] Updated weights for policy 0, policy_version 6490 (0.0008)
+[2023-09-11 21:51:15,208][83013] Updated weights for policy 0, policy_version 6500 (0.0008)
+[2023-09-11 21:51:16,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11537.1, 300 sec: 12093.6). Total num frames: 26636288. Throughput: 0: 2879.6. Samples: 5655150. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:51:16,385][88175] Avg episode reward: [(0, '10.120')]
+[2023-09-11 21:51:18,749][83013] Updated weights for policy 0, policy_version 6510 (0.0009)
+[2023-09-11 21:51:21,384][88175] Fps is (10 sec: 11468.4, 60 sec: 11537.0, 300 sec: 12093.6). Total num frames: 26693632. Throughput: 0: 2884.0. Samples: 5663960. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:51:21,385][88175] Avg episode reward: [(0, '10.000')]
+[2023-09-11 21:51:22,308][83013] Updated weights for policy 0, policy_version 6520 (0.0009)
+[2023-09-11 21:51:25,868][83013] Updated weights for policy 0, policy_version 6530 (0.0008)
+[2023-09-11 21:51:26,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11537.1, 300 sec: 12093.6). Total num frames: 26750976. Throughput: 0: 2883.5. Samples: 5680984. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:51:26,385][88175] Avg episode reward: [(0, '10.000')]
+[2023-09-11 21:51:29,369][83013] Updated weights for policy 0, policy_version 6540 (0.0009)
+[2023-09-11 21:51:31,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11537.1, 300 sec: 12093.6). Total num frames: 26808320. Throughput: 0: 2893.8. Samples: 5698680. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:51:31,385][88175] Avg episode reward: [(0, '9.990')]
+[2023-09-11 21:51:32,891][83013] Updated weights for policy 0, policy_version 6550 (0.0008)
+[2023-09-11 21:51:36,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11537.1, 300 sec: 12093.6). Total num frames: 26865664. Throughput: 0: 2891.3. Samples: 5707420. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:51:36,385][88175] Avg episode reward: [(0, '10.250')]
+[2023-09-11 21:51:36,474][83013] Updated weights for policy 0, policy_version 6560 (0.0009)
+[2023-09-11 21:51:40,005][83013] Updated weights for policy 0, policy_version 6570 (0.0009)
+[2023-09-11 21:51:41,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12093.6). Total num frames: 26923008. Throughput: 0: 2891.8. Samples: 5724634. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:51:41,385][88175] Avg episode reward: [(0, '10.260')]
+[2023-09-11 21:51:43,588][83013] Updated weights for policy 0, policy_version 6580 (0.0009)
+[2023-09-11 21:51:46,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11605.3, 300 sec: 12121.4). Total num frames: 26984448. Throughput: 0: 2889.2. Samples: 5741976. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:51:46,385][88175] Avg episode reward: [(0, '10.180')]
+[2023-09-11 21:51:47,065][83013] Updated weights for policy 0, policy_version 6590 (0.0009)
+[2023-09-11 21:51:50,680][83013] Updated weights for policy 0, policy_version 6600 (0.0010)
+[2023-09-11 21:51:51,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11605.4, 300 sec: 12107.5). Total num frames: 27041792. Throughput: 0: 2886.9. Samples: 5750372. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:51:51,384][88175] Avg episode reward: [(0, '10.310')]
+[2023-09-11 21:51:54,225][83013] Updated weights for policy 0, policy_version 6610 (0.0009)
+[2023-09-11 21:51:56,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11605.3, 300 sec: 12121.4). Total num frames: 27099136. Throughput: 0: 2889.6. Samples: 5767824. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:51:56,385][88175] Avg episode reward: [(0, '10.560')]
+[2023-09-11 21:51:57,772][83013] Updated weights for policy 0, policy_version 6620 (0.0008)
+[2023-09-11 21:52:01,305][83013] Updated weights for policy 0, policy_version 6630 (0.0008)
+[2023-09-11 21:52:01,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11605.3, 300 sec: 12107.5). Total num frames: 27156480. Throughput: 0: 2888.8. Samples: 5785144. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:52:01,385][88175] Avg episode reward: [(0, '10.300')]
+[2023-09-11 21:52:04,851][83013] Updated weights for policy 0, policy_version 6640 (0.0008)
+[2023-09-11 21:52:06,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11605.4, 300 sec: 12107.5). Total num frames: 27213824. Throughput: 0: 2881.6. Samples: 5793630. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:52:06,385][88175] Avg episode reward: [(0, '10.370')]
+[2023-09-11 21:52:06,391][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000006644_27213824.pth...
+[2023-09-11 21:52:06,443][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000005925_24268800.pth
+[2023-09-11 21:52:08,365][83013] Updated weights for policy 0, policy_version 6650 (0.0009)
+[2023-09-11 21:52:11,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.0, 300 sec: 12107.5). Total num frames: 27271168. Throughput: 0: 2890.0. Samples: 5811032. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:52:11,385][88175] Avg episode reward: [(0, '10.320')]
+[2023-09-11 21:52:11,957][83013] Updated weights for policy 0, policy_version 6660 (0.0008)
+[2023-09-11 21:52:14,500][83013] Updated weights for policy 0, policy_version 6670 (0.0008)
+[2023-09-11 21:52:16,384][88175] Fps is (10 sec: 13516.6, 60 sec: 11878.4, 300 sec: 12176.9). Total num frames: 27348992. Throughput: 0: 2989.1. Samples: 5833192. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 21:52:16,385][88175] Avg episode reward: [(0, '10.400')]
+[2023-09-11 21:52:16,966][83013] Updated weights for policy 0, policy_version 6680 (0.0010)
+[2023-09-11 21:52:19,424][83013] Updated weights for policy 0, policy_version 6690 (0.0009)
+[2023-09-11 21:52:21,384][88175] Fps is (10 sec: 16384.0, 60 sec: 12356.3, 300 sec: 12274.1). Total num frames: 27435008. Throughput: 0: 3071.7. Samples: 5845648. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:52:21,385][88175] Avg episode reward: [(0, '10.240')]
+[2023-09-11 21:52:21,879][83013] Updated weights for policy 0, policy_version 6700 (0.0008)
+[2023-09-11 21:52:24,337][83013] Updated weights for policy 0, policy_version 6710 (0.0008)
+[2023-09-11 21:52:26,383][88175] Fps is (10 sec: 16794.0, 60 sec: 12765.9, 300 sec: 12371.3). Total num frames: 27516928. Throughput: 0: 3240.4. Samples: 5870450. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:52:26,384][88175] Avg episode reward: [(0, '10.260')]
+[2023-09-11 21:52:26,809][83013] Updated weights for policy 0, policy_version 6720 (0.0008)
+[2023-09-11 21:52:29,292][83013] Updated weights for policy 0, policy_version 6730 (0.0009)
+[2023-09-11 21:52:31,383][88175] Fps is (10 sec: 16384.3, 60 sec: 13175.5, 300 sec: 12440.7). Total num frames: 27598848. Throughput: 0: 3412.1. Samples: 5895520. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:52:31,385][88175] Avg episode reward: [(0, '10.150')]
+[2023-09-11 21:52:31,725][83013] Updated weights for policy 0, policy_version 6740 (0.0008)
+[2023-09-11 21:52:34,201][83013] Updated weights for policy 0, policy_version 6750 (0.0009)
+[2023-09-11 21:52:36,384][88175] Fps is (10 sec: 16383.5, 60 sec: 13585.0, 300 sec: 12537.9). Total num frames: 27680768. Throughput: 0: 3499.8. Samples: 5907862. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:52:36,385][88175] Avg episode reward: [(0, '10.230')]
+[2023-09-11 21:52:36,761][83013] Updated weights for policy 0, policy_version 6760 (0.0008)
+[2023-09-11 21:52:40,226][83013] Updated weights for policy 0, policy_version 6770 (0.0008)
+[2023-09-11 21:52:41,383][88175] Fps is (10 sec: 14336.0, 60 sec: 13653.3, 300 sec: 12551.8). Total num frames: 27742208. Throughput: 0: 3573.5. Samples: 5928632. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:52:41,385][88175] Avg episode reward: [(0, '10.420')]
+[2023-09-11 21:52:43,803][83013] Updated weights for policy 0, policy_version 6780 (0.0008)
+[2023-09-11 21:52:46,384][88175] Fps is (10 sec: 11878.3, 60 sec: 13585.0, 300 sec: 12551.8). Total num frames: 27799552. Throughput: 0: 3573.9. Samples: 5945972. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:52:46,385][88175] Avg episode reward: [(0, '10.530')]
+[2023-09-11 21:52:47,410][83013] Updated weights for policy 0, policy_version 6790 (0.0009)
+[2023-09-11 21:52:50,958][83013] Updated weights for policy 0, policy_version 6800 (0.0008)
+[2023-09-11 21:52:51,383][88175] Fps is (10 sec: 11468.9, 60 sec: 13585.1, 300 sec: 12551.8). Total num frames: 27856896. Throughput: 0: 3574.2. Samples: 5954470. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:52:51,385][88175] Avg episode reward: [(0, '10.590')]
+[2023-09-11 21:52:54,536][83013] Updated weights for policy 0, policy_version 6810 (0.0008)
+[2023-09-11 21:52:56,384][88175] Fps is (10 sec: 11469.1, 60 sec: 13585.0, 300 sec: 12551.8). Total num frames: 27914240. Throughput: 0: 3567.9. Samples: 5971586. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:52:56,385][88175] Avg episode reward: [(0, '10.400')]
+[2023-09-11 21:52:58,043][83013] Updated weights for policy 0, policy_version 6820 (0.0009)
+[2023-09-11 21:53:01,383][88175] Fps is (10 sec: 11468.8, 60 sec: 13585.1, 300 sec: 12551.8). Total num frames: 27971584. Throughput: 0: 3462.6. Samples: 5989010. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:53:01,384][88175] Avg episode reward: [(0, '10.520')]
+[2023-09-11 21:53:01,636][83013] Updated weights for policy 0, policy_version 6830 (0.0009)
+[2023-09-11 21:53:05,278][83013] Updated weights for policy 0, policy_version 6840 (0.0008)
+[2023-09-11 21:53:06,383][88175] Fps is (10 sec: 11469.0, 60 sec: 13585.1, 300 sec: 12551.8). Total num frames: 28028928. Throughput: 0: 3374.2. Samples: 5997486. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:53:06,385][88175] Avg episode reward: [(0, '10.670')]
+[2023-09-11 21:53:08,711][83013] Updated weights for policy 0, policy_version 6850 (0.0008)
+[2023-09-11 21:53:11,383][88175] Fps is (10 sec: 11468.8, 60 sec: 13585.1, 300 sec: 12551.8). Total num frames: 28086272. Throughput: 0: 3213.2. Samples: 6015046. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:53:11,385][88175] Avg episode reward: [(0, '10.410')]
+[2023-09-11 21:53:12,328][83013] Updated weights for policy 0, policy_version 6860 (0.0008)
+[2023-09-11 21:53:15,877][83013] Updated weights for policy 0, policy_version 6870 (0.0008)
+[2023-09-11 21:53:16,383][88175] Fps is (10 sec: 11468.7, 60 sec: 13243.8, 300 sec: 12551.8). Total num frames: 28143616. Throughput: 0: 3034.2. Samples: 6032060. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:53:16,385][88175] Avg episode reward: [(0, '10.670')]
+[2023-09-11 21:53:19,432][83013] Updated weights for policy 0, policy_version 6880 (0.0008)
+[2023-09-11 21:53:21,383][88175] Fps is (10 sec: 11468.7, 60 sec: 12765.9, 300 sec: 12551.8). Total num frames: 28200960. Throughput: 0: 2952.7. Samples: 6040732. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:53:21,385][88175] Avg episode reward: [(0, '10.470')]
+[2023-09-11 21:53:23,043][83013] Updated weights for policy 0, policy_version 6890 (0.0010)
+[2023-09-11 21:53:26,383][88175] Fps is (10 sec: 11468.9, 60 sec: 12356.3, 300 sec: 12551.8). Total num frames: 28258304. Throughput: 0: 2870.1. Samples: 6057788. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:53:26,385][88175] Avg episode reward: [(0, '10.270')]
+[2023-09-11 21:53:26,610][83013] Updated weights for policy 0, policy_version 6900 (0.0008)
+[2023-09-11 21:53:30,181][83013] Updated weights for policy 0, policy_version 6910 (0.0008)
+[2023-09-11 21:53:31,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11946.7, 300 sec: 12537.9). Total num frames: 28315648. Throughput: 0: 2868.2. Samples: 6075040. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:53:31,385][88175] Avg episode reward: [(0, '10.550')]
+[2023-09-11 21:53:33,774][83013] Updated weights for policy 0, policy_version 6920 (0.0009)
+[2023-09-11 21:53:36,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11537.1, 300 sec: 12551.8). Total num frames: 28372992. Throughput: 0: 2871.3. Samples: 6083680. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:53:36,385][88175] Avg episode reward: [(0, '10.560')]
+[2023-09-11 21:53:37,332][83013] Updated weights for policy 0, policy_version 6930 (0.0008)
+[2023-09-11 21:53:40,931][83013] Updated weights for policy 0, policy_version 6940 (0.0008)
+[2023-09-11 21:53:41,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12537.9). Total num frames: 28430336. Throughput: 0: 2872.4. Samples: 6100842. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:53:41,385][88175] Avg episode reward: [(0, '10.660')]
+[2023-09-11 21:53:44,516][83013] Updated weights for policy 0, policy_version 6950 (0.0008)
+[2023-09-11 21:53:46,384][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12524.0). Total num frames: 28487680. Throughput: 0: 2867.6. Samples: 6118052. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:53:46,385][88175] Avg episode reward: [(0, '10.430')]
+[2023-09-11 21:53:48,133][83013] Updated weights for policy 0, policy_version 6960 (0.0009)
+[2023-09-11 21:53:51,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12440.7). Total num frames: 28545024. Throughput: 0: 2864.8. Samples: 6126404. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:53:51,385][88175] Avg episode reward: [(0, '10.600')]
+[2023-09-11 21:53:51,637][83013] Updated weights for policy 0, policy_version 6970 (0.0008)
+[2023-09-11 21:53:55,210][83013] Updated weights for policy 0, policy_version 6980 (0.0008)
+[2023-09-11 21:53:56,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12357.4). Total num frames: 28602368. Throughput: 0: 2860.7. Samples: 6143778. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:53:56,385][88175] Avg episode reward: [(0, '10.830')]
+[2023-09-11 21:53:58,705][83013] Updated weights for policy 0, policy_version 6990 (0.0009)
+[2023-09-11 21:54:01,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12260.2). Total num frames: 28659712. Throughput: 0: 2869.9. Samples: 6161204. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:54:01,385][88175] Avg episode reward: [(0, '10.610')]
+[2023-09-11 21:54:02,227][83013] Updated weights for policy 0, policy_version 7000 (0.0008)
+[2023-09-11 21:54:05,777][83013] Updated weights for policy 0, policy_version 7010 (0.0008)
+[2023-09-11 21:54:06,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12176.9). Total num frames: 28717056. Throughput: 0: 2872.5. Samples: 6169996. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:54:06,385][88175] Avg episode reward: [(0, '10.680')]
+[2023-09-11 21:54:06,390][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000007011_28717056.pth...
+[2023-09-11 21:54:06,451][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000006306_25829376.pth
+[2023-09-11 21:54:09,426][83013] Updated weights for policy 0, policy_version 7020 (0.0008)
+[2023-09-11 21:54:11,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12093.6). Total num frames: 28774400. Throughput: 0: 2872.6. Samples: 6187054. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:54:11,385][88175] Avg episode reward: [(0, '10.610')]
+[2023-09-11 21:54:13,037][83013] Updated weights for policy 0, policy_version 7030 (0.0008)
+[2023-09-11 21:54:16,384][88175] Fps is (10 sec: 11468.4, 60 sec: 11468.7, 300 sec: 11996.4). Total num frames: 28831744. Throughput: 0: 2866.8. Samples: 6204048. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:54:16,386][88175] Avg episode reward: [(0, '10.940')]
+[2023-09-11 21:54:16,664][83013] Updated weights for policy 0, policy_version 7040 (0.0008)
+[2023-09-11 21:54:20,202][83013] Updated weights for policy 0, policy_version 7050 (0.0008)
+[2023-09-11 21:54:21,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 28889088. Throughput: 0: 2866.1. Samples: 6212654. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:54:21,385][88175] Avg episode reward: [(0, '10.810')]
+[2023-09-11 21:54:23,741][83013] Updated weights for policy 0, policy_version 7060 (0.0008)
+[2023-09-11 21:54:26,384][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 28946432. Throughput: 0: 2863.2. Samples: 6229686. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:54:26,388][88175] Avg episode reward: [(0, '10.650')]
+[2023-09-11 21:54:27,341][83013] Updated weights for policy 0, policy_version 7070 (0.0009)
+[2023-09-11 21:54:30,912][83013] Updated weights for policy 0, policy_version 7080 (0.0008)
+[2023-09-11 21:54:31,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 29003776. Throughput: 0: 2867.9. Samples: 6247108. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:54:31,385][88175] Avg episode reward: [(0, '10.570')]
+[2023-09-11 21:54:34,440][83013] Updated weights for policy 0, policy_version 7090 (0.0008)
+[2023-09-11 21:54:36,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.9, 300 sec: 11927.0). Total num frames: 29061120. Throughput: 0: 2874.3. Samples: 6255748. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:54:36,385][88175] Avg episode reward: [(0, '10.410')]
+[2023-09-11 21:54:38,017][83013] Updated weights for policy 0, policy_version 7100 (0.0008)
+[2023-09-11 21:54:41,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 29118464. Throughput: 0: 2870.0. Samples: 6272926. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:54:41,385][88175] Avg episode reward: [(0, '10.860')]
+[2023-09-11 21:54:41,609][83013] Updated weights for policy 0, policy_version 7110 (0.0008)
+[2023-09-11 21:54:45,170][83013] Updated weights for policy 0, policy_version 7120 (0.0008)
+[2023-09-11 21:54:46,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 29175808. Throughput: 0: 2866.6. Samples: 6290202. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:54:46,385][88175] Avg episode reward: [(0, '10.940')]
+[2023-09-11 21:54:48,657][83013] Updated weights for policy 0, policy_version 7130 (0.0009)
+[2023-09-11 21:54:51,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 29233152. Throughput: 0: 2865.2. Samples: 6298928. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:54:51,384][88175] Avg episode reward: [(0, '10.530')]
+[2023-09-11 21:54:52,215][83013] Updated weights for policy 0, policy_version 7140 (0.0008)
+[2023-09-11 21:54:55,830][83013] Updated weights for policy 0, policy_version 7150 (0.0008)
+[2023-09-11 21:54:56,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 29290496. Throughput: 0: 2869.1. Samples: 6316164. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:54:56,385][88175] Avg episode reward: [(0, '10.780')]
+[2023-09-11 21:54:59,380][83013] Updated weights for policy 0, policy_version 7160 (0.0008)
+[2023-09-11 21:55:01,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 29347840. Throughput: 0: 2873.9. Samples: 6333374. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:55:01,384][88175] Avg episode reward: [(0, '10.700')]
+[2023-09-11 21:55:02,953][83013] Updated weights for policy 0, policy_version 7170 (0.0008)
+[2023-09-11 21:55:06,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.7, 300 sec: 11927.0). Total num frames: 29405184. Throughput: 0: 2872.8. Samples: 6341932. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:55:06,395][88175] Avg episode reward: [(0, '10.880')]
+[2023-09-11 21:55:06,502][83013] Updated weights for policy 0, policy_version 7180 (0.0008)
+[2023-09-11 21:55:10,043][83013] Updated weights for policy 0, policy_version 7190 (0.0009)
+[2023-09-11 21:55:11,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 29462528. Throughput: 0: 2878.5. Samples: 6359216. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:55:11,385][88175] Avg episode reward: [(0, '10.920')]
+[2023-09-11 21:55:13,642][83013] Updated weights for policy 0, policy_version 7200 (0.0009)
+[2023-09-11 21:55:16,384][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 29519872. Throughput: 0: 2872.3. Samples: 6376360. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:55:16,385][88175] Avg episode reward: [(0, '10.650')]
+[2023-09-11 21:55:17,230][83013] Updated weights for policy 0, policy_version 7210 (0.0008)
+[2023-09-11 21:55:20,827][83013] Updated weights for policy 0, policy_version 7220 (0.0008)
+[2023-09-11 21:55:21,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 11927.0). Total num frames: 29577216. Throughput: 0: 2875.2. Samples: 6385130. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:55:21,385][88175] Avg episode reward: [(0, '10.850')]
+[2023-09-11 21:55:23,790][83013] Updated weights for policy 0, policy_version 7230 (0.0008)
+[2023-09-11 21:55:26,240][83013] Updated weights for policy 0, policy_version 7240 (0.0009)
+[2023-09-11 21:55:26,383][88175] Fps is (10 sec: 13517.1, 60 sec: 11810.2, 300 sec: 11996.4). Total num frames: 29655040. Throughput: 0: 2941.9. Samples: 6405310. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:55:26,385][88175] Avg episode reward: [(0, '10.830')]
+[2023-09-11 21:55:28,722][83013] Updated weights for policy 0, policy_version 7250 (0.0009)
+[2023-09-11 21:55:31,170][83013] Updated weights for policy 0, policy_version 7260 (0.0009)
+[2023-09-11 21:55:31,384][88175] Fps is (10 sec: 15974.0, 60 sec: 12219.7, 300 sec: 12079.7). Total num frames: 29736960. Throughput: 0: 3114.0. Samples: 6430334. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 21:55:31,385][88175] Avg episode reward: [(0, '11.150')]
+[2023-09-11 21:55:33,634][83013] Updated weights for policy 0, policy_version 7270 (0.0008)
+[2023-09-11 21:55:36,117][83013] Updated weights for policy 0, policy_version 7280 (0.0008)
+[2023-09-11 21:55:36,384][88175] Fps is (10 sec: 16793.4, 60 sec: 12697.6, 300 sec: 12176.9). Total num frames: 29822976. Throughput: 0: 3195.5. Samples: 6442728. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:55:36,386][88175] Avg episode reward: [(0, '11.050')]
+[2023-09-11 21:55:38,543][83013] Updated weights for policy 0, policy_version 7290 (0.0008)
+[2023-09-11 21:55:40,996][83013] Updated weights for policy 0, policy_version 7300 (0.0008)
+[2023-09-11 21:55:41,384][88175] Fps is (10 sec: 16793.8, 60 sec: 13107.2, 300 sec: 12260.2). Total num frames: 29904896. Throughput: 0: 3373.2. Samples: 6467958. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:55:41,385][88175] Avg episode reward: [(0, '11.100')]
+[2023-09-11 21:55:43,448][83013] Updated weights for policy 0, policy_version 7310 (0.0008)
+[2023-09-11 21:55:45,929][83013] Updated weights for policy 0, policy_version 7320 (0.0007)
+[2023-09-11 21:55:46,383][88175] Fps is (10 sec: 16384.0, 60 sec: 13516.8, 300 sec: 12343.5). Total num frames: 29986816. Throughput: 0: 3544.0. Samples: 6492856. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:55:46,386][88175] Avg episode reward: [(0, '11.150')]
+[2023-09-11 21:55:48,410][83013] Updated weights for policy 0, policy_version 7330 (0.0008)
+[2023-09-11 21:55:50,882][83013] Updated weights for policy 0, policy_version 7340 (0.0009)
+[2023-09-11 21:55:51,383][88175] Fps is (10 sec: 16384.3, 60 sec: 13926.4, 300 sec: 12426.9). Total num frames: 30068736. Throughput: 0: 3627.6. Samples: 6505174. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:55:51,385][88175] Avg episode reward: [(0, '11.230')]
+[2023-09-11 21:55:53,346][83013] Updated weights for policy 0, policy_version 7350 (0.0009)
+[2023-09-11 21:55:55,847][83013] Updated weights for policy 0, policy_version 7360 (0.0008)
+[2023-09-11 21:55:56,384][88175] Fps is (10 sec: 16793.5, 60 sec: 14404.3, 300 sec: 12524.0). Total num frames: 30154752. Throughput: 0: 3792.7. Samples: 6529886. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:55:56,385][88175] Avg episode reward: [(0, '10.950')]
+[2023-09-11 21:55:58,351][83013] Updated weights for policy 0, policy_version 7370 (0.0009)
+[2023-09-11 21:56:00,786][83013] Updated weights for policy 0, policy_version 7380 (0.0009)
+[2023-09-11 21:56:01,383][88175] Fps is (10 sec: 16793.4, 60 sec: 14813.8, 300 sec: 12607.4). Total num frames: 30236672. Throughput: 0: 3965.4. Samples: 6554804. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:56:01,385][88175] Avg episode reward: [(0, '11.300')]
+[2023-09-11 21:56:03,235][83013] Updated weights for policy 0, policy_version 7390 (0.0008)
+[2023-09-11 21:56:06,177][83013] Updated weights for policy 0, policy_version 7400 (0.0009)
+[2023-09-11 21:56:06,384][88175] Fps is (10 sec: 15564.5, 60 sec: 15086.9, 300 sec: 12649.0). Total num frames: 30310400. Throughput: 0: 4050.2. Samples: 6567392. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:56:06,385][88175] Avg episode reward: [(0, '11.290')]
+[2023-09-11 21:56:06,393][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000007400_30310400.pth...
+[2023-09-11 21:56:06,444][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000006644_27213824.pth
+[2023-09-11 21:56:09,664][83013] Updated weights for policy 0, policy_version 7410 (0.0009)
+[2023-09-11 21:56:11,383][88175] Fps is (10 sec: 13107.4, 60 sec: 15087.0, 300 sec: 12649.0). Total num frames: 30367744. Throughput: 0: 4008.5. Samples: 6585692. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:56:11,385][88175] Avg episode reward: [(0, '11.300')]
+[2023-09-11 21:56:13,296][83013] Updated weights for policy 0, policy_version 7420 (0.0009)
+[2023-09-11 21:56:16,383][88175] Fps is (10 sec: 11469.2, 60 sec: 15087.0, 300 sec: 12649.0). Total num frames: 30425088. Throughput: 0: 3833.5. Samples: 6602840. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:56:16,385][88175] Avg episode reward: [(0, '11.370')]
+[2023-09-11 21:56:16,854][83013] Updated weights for policy 0, policy_version 7430 (0.0008)
+[2023-09-11 21:56:20,367][83013] Updated weights for policy 0, policy_version 7440 (0.0009)
+[2023-09-11 21:56:21,383][88175] Fps is (10 sec: 11468.6, 60 sec: 15086.9, 300 sec: 12649.0). Total num frames: 30482432. Throughput: 0: 3752.3. Samples: 6611580. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:56:21,385][88175] Avg episode reward: [(0, '11.180')]
+[2023-09-11 21:56:23,934][83013] Updated weights for policy 0, policy_version 7450 (0.0008)
+[2023-09-11 21:56:26,384][88175] Fps is (10 sec: 11468.6, 60 sec: 14745.5, 300 sec: 12649.0). Total num frames: 30539776. Throughput: 0: 3576.4. Samples: 6628896. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:56:26,385][88175] Avg episode reward: [(0, '11.120')]
+[2023-09-11 21:56:27,483][83013] Updated weights for policy 0, policy_version 7460 (0.0009)
+[2023-09-11 21:56:31,077][83013] Updated weights for policy 0, policy_version 7470 (0.0008)
+[2023-09-11 21:56:31,383][88175] Fps is (10 sec: 11468.8, 60 sec: 14336.0, 300 sec: 12649.0). Total num frames: 30597120. Throughput: 0: 3400.2. Samples: 6645866. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:56:31,384][88175] Avg episode reward: [(0, '11.360')]
+[2023-09-11 21:56:34,684][83013] Updated weights for policy 0, policy_version 7480 (0.0009)
+[2023-09-11 21:56:36,383][88175] Fps is (10 sec: 11468.9, 60 sec: 13858.1, 300 sec: 12649.0). Total num frames: 30654464. Throughput: 0: 3316.6. Samples: 6654420. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:56:36,385][88175] Avg episode reward: [(0, '11.590')]
+[2023-09-11 21:56:38,247][83013] Updated weights for policy 0, policy_version 7490 (0.0009)
+[2023-09-11 21:56:41,383][88175] Fps is (10 sec: 11468.8, 60 sec: 13448.6, 300 sec: 12635.1). Total num frames: 30711808. Throughput: 0: 3149.5. Samples: 6671612. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:56:41,385][88175] Avg episode reward: [(0, '11.270')]
+[2023-09-11 21:56:41,797][83013] Updated weights for policy 0, policy_version 7500 (0.0009)
+[2023-09-11 21:56:45,407][83013] Updated weights for policy 0, policy_version 7510 (0.0009)
+[2023-09-11 21:56:46,384][88175] Fps is (10 sec: 11468.6, 60 sec: 13038.9, 300 sec: 12635.1). Total num frames: 30769152. Throughput: 0: 2978.1. Samples: 6688820. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:56:46,385][88175] Avg episode reward: [(0, '11.300')]
+[2023-09-11 21:56:48,976][83013] Updated weights for policy 0, policy_version 7520 (0.0008)
+[2023-09-11 21:56:51,383][88175] Fps is (10 sec: 11468.8, 60 sec: 12629.3, 300 sec: 12635.1). Total num frames: 30826496. Throughput: 0: 2893.9. Samples: 6697618. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:56:51,385][88175] Avg episode reward: [(0, '11.340')]
+[2023-09-11 21:56:52,533][83013] Updated weights for policy 0, policy_version 7530 (0.0009)
+[2023-09-11 21:56:56,067][83013] Updated weights for policy 0, policy_version 7540 (0.0009)
+[2023-09-11 21:56:56,384][88175] Fps is (10 sec: 11468.9, 60 sec: 12151.5, 300 sec: 12635.1). Total num frames: 30883840. Throughput: 0: 2864.6. Samples: 6714600. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:56:56,386][88175] Avg episode reward: [(0, '11.420')]
+[2023-09-11 21:56:59,700][83013] Updated weights for policy 0, policy_version 7550 (0.0008)
+[2023-09-11 21:57:01,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11741.9, 300 sec: 12635.1). Total num frames: 30941184. Throughput: 0: 2864.3. Samples: 6731732. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 21:57:01,385][88175] Avg episode reward: [(0, '11.350')]
+[2023-09-11 21:57:03,330][83013] Updated weights for policy 0, policy_version 7560 (0.0008)
+[2023-09-11 21:57:06,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.9, 300 sec: 12635.1). Total num frames: 30998528. Throughput: 0: 2861.2. Samples: 6740332. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:57:06,384][88175] Avg episode reward: [(0, '11.280')]
+[2023-09-11 21:57:06,922][83013] Updated weights for policy 0, policy_version 7570 (0.0008)
+[2023-09-11 21:57:10,449][83013] Updated weights for policy 0, policy_version 7580 (0.0008)
+[2023-09-11 21:57:11,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11468.7, 300 sec: 12565.7). Total num frames: 31055872. Throughput: 0: 2857.9. Samples: 6757500. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:57:11,385][88175] Avg episode reward: [(0, '11.670')]
+[2023-09-11 21:57:14,007][83013] Updated weights for policy 0, policy_version 7590 (0.0008)
+[2023-09-11 21:57:16,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 12468.5). Total num frames: 31113216. Throughput: 0: 2863.9. Samples: 6774742. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:57:16,385][88175] Avg episode reward: [(0, '11.410')]
+[2023-09-11 21:57:17,566][83013] Updated weights for policy 0, policy_version 7600 (0.0008)
+[2023-09-11 21:57:21,080][83013] Updated weights for policy 0, policy_version 7610 (0.0008)
+[2023-09-11 21:57:21,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 12385.2). Total num frames: 31170560. Throughput: 0: 2869.5. Samples: 6783548. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:57:21,384][88175] Avg episode reward: [(0, '11.510')]
+[2023-09-11 21:57:24,707][83013] Updated weights for policy 0, policy_version 7620 (0.0008)
+[2023-09-11 21:57:26,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12301.9). Total num frames: 31227904. Throughput: 0: 2867.7. Samples: 6800660. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:57:26,385][88175] Avg episode reward: [(0, '11.520')]
+[2023-09-11 21:57:28,296][83013] Updated weights for policy 0, policy_version 7630 (0.0008)
+[2023-09-11 21:57:31,384][88175] Fps is (10 sec: 11468.4, 60 sec: 11468.7, 300 sec: 12218.6). Total num frames: 31285248. Throughput: 0: 2863.4. Samples: 6817674. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:57:31,385][88175] Avg episode reward: [(0, '11.440')]
+[2023-09-11 21:57:31,881][83013] Updated weights for policy 0, policy_version 7640 (0.0009)
+[2023-09-11 21:57:35,472][83013] Updated weights for policy 0, policy_version 7650 (0.0008)
+[2023-09-11 21:57:36,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 12204.7). Total num frames: 31342592. Throughput: 0: 2861.5. Samples: 6826384. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:57:36,385][88175] Avg episode reward: [(0, '11.500')]
+[2023-09-11 21:57:39,012][83013] Updated weights for policy 0, policy_version 7660 (0.0008)
+[2023-09-11 21:57:41,384][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12204.7). Total num frames: 31399936. Throughput: 0: 2869.4. Samples: 6843722. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:57:41,385][88175] Avg episode reward: [(0, '11.570')]
+[2023-09-11 21:57:42,522][83013] Updated weights for policy 0, policy_version 7670 (0.0009)
+[2023-09-11 21:57:46,075][83013] Updated weights for policy 0, policy_version 7680 (0.0009)
+[2023-09-11 21:57:46,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12204.7). Total num frames: 31457280. Throughput: 0: 2875.1. Samples: 6861110. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:57:46,385][88175] Avg episode reward: [(0, '11.380')]
+[2023-09-11 21:57:49,522][83013] Updated weights for policy 0, policy_version 7690 (0.0008)
+[2023-09-11 21:57:51,383][88175] Fps is (10 sec: 11878.6, 60 sec: 11537.1, 300 sec: 12218.6). Total num frames: 31518720. Throughput: 0: 2877.3. Samples: 6869810. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:57:51,385][88175] Avg episode reward: [(0, '11.570')]
+[2023-09-11 21:57:53,070][83013] Updated weights for policy 0, policy_version 7700 (0.0009)
+[2023-09-11 21:57:56,384][88175] Fps is (10 sec: 11878.1, 60 sec: 11537.1, 300 sec: 12218.6). Total num frames: 31576064. Throughput: 0: 2880.2. Samples: 6887110. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:57:56,386][88175] Avg episode reward: [(0, '11.420')]
+[2023-09-11 21:57:56,657][83013] Updated weights for policy 0, policy_version 7710 (0.0008)
+[2023-09-11 21:58:00,228][83013] Updated weights for policy 0, policy_version 7720 (0.0009)
+[2023-09-11 21:58:01,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12218.6). Total num frames: 31633408. Throughput: 0: 2878.9. Samples: 6904294. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:58:01,385][88175] Avg episode reward: [(0, '11.710')]
+[2023-09-11 21:58:03,894][83013] Updated weights for policy 0, policy_version 7730 (0.0008)
+[2023-09-11 21:58:06,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11537.0, 300 sec: 12218.6). Total num frames: 31690752. Throughput: 0: 2869.4. Samples: 6912670. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:58:06,385][88175] Avg episode reward: [(0, '11.650')]
+[2023-09-11 21:58:06,390][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000007737_31690752.pth...
+[2023-09-11 21:58:06,440][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000007011_28717056.pth
+[2023-09-11 21:58:07,361][83013] Updated weights for policy 0, policy_version 7740 (0.0008)
+[2023-09-11 21:58:10,815][83013] Updated weights for policy 0, policy_version 7750 (0.0009)
+[2023-09-11 21:58:11,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11537.1, 300 sec: 12218.6). Total num frames: 31748096. Throughput: 0: 2885.7. Samples: 6930518. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:58:11,385][88175] Avg episode reward: [(0, '11.600')]
+[2023-09-11 21:58:14,337][83013] Updated weights for policy 0, policy_version 7760 (0.0009)
+[2023-09-11 21:58:16,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12218.6). Total num frames: 31805440. Throughput: 0: 2894.9. Samples: 6947944. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:58:16,385][88175] Avg episode reward: [(0, '11.490')]
+[2023-09-11 21:58:17,913][83013] Updated weights for policy 0, policy_version 7770 (0.0008)
+[2023-09-11 21:58:21,384][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.0, 300 sec: 12218.6). Total num frames: 31862784. Throughput: 0: 2894.7. Samples: 6956646. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:58:21,385][88175] Avg episode reward: [(0, '11.490')]
+[2023-09-11 21:58:21,444][83013] Updated weights for policy 0, policy_version 7780 (0.0010)
+[2023-09-11 21:58:25,072][83013] Updated weights for policy 0, policy_version 7790 (0.0008)
+[2023-09-11 21:58:26,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11537.1, 300 sec: 12218.6). Total num frames: 31920128. Throughput: 0: 2888.6. Samples: 6973708. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:58:26,385][88175] Avg episode reward: [(0, '11.310')]
+[2023-09-11 21:58:28,630][83013] Updated weights for policy 0, policy_version 7800 (0.0009)
+[2023-09-11 21:58:31,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.1, 300 sec: 12218.6). Total num frames: 31977472. Throughput: 0: 2885.1. Samples: 6990940. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:58:31,385][88175] Avg episode reward: [(0, '11.400')]
+[2023-09-11 21:58:32,134][83013] Updated weights for policy 0, policy_version 7810 (0.0008)
+[2023-09-11 21:58:35,735][83013] Updated weights for policy 0, policy_version 7820 (0.0009)
+[2023-09-11 21:58:36,384][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.0, 300 sec: 12218.6). Total num frames: 32034816. Throughput: 0: 2884.9. Samples: 6999630. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:58:36,385][88175] Avg episode reward: [(0, '11.590')]
+[2023-09-11 21:58:39,301][83013] Updated weights for policy 0, policy_version 7830 (0.0008)
+[2023-09-11 21:58:41,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11605.4, 300 sec: 12232.5). Total num frames: 32096256. Throughput: 0: 2886.5. Samples: 7017004. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:58:41,385][88175] Avg episode reward: [(0, '11.740')]
+[2023-09-11 21:58:42,742][83013] Updated weights for policy 0, policy_version 7840 (0.0009)
+[2023-09-11 21:58:46,304][83013] Updated weights for policy 0, policy_version 7850 (0.0008)
+[2023-09-11 21:58:46,384][88175] Fps is (10 sec: 11878.5, 60 sec: 11605.3, 300 sec: 12232.5). Total num frames: 32153600. Throughput: 0: 2896.7. Samples: 7034648. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:58:46,385][88175] Avg episode reward: [(0, '11.920')]
+[2023-09-11 21:58:49,805][83013] Updated weights for policy 0, policy_version 7860 (0.0009)
+[2023-09-11 21:58:51,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.1, 300 sec: 12232.5). Total num frames: 32210944. Throughput: 0: 2901.8. Samples: 7043250. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:58:51,384][88175] Avg episode reward: [(0, '12.060')]
+[2023-09-11 21:58:52,670][83013] Updated weights for policy 0, policy_version 7870 (0.0008)
+[2023-09-11 21:58:55,114][83013] Updated weights for policy 0, policy_version 7880 (0.0008)
+[2023-09-11 21:58:56,384][88175] Fps is (10 sec: 14335.9, 60 sec: 12014.9, 300 sec: 12329.6). Total num frames: 32296960. Throughput: 0: 2994.2. Samples: 7065256. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:58:56,385][88175] Avg episode reward: [(0, '11.590')]
+[2023-09-11 21:58:57,620][83013] Updated weights for policy 0, policy_version 7890 (0.0008)
+[2023-09-11 21:59:00,050][83013] Updated weights for policy 0, policy_version 7900 (0.0008)
+[2023-09-11 21:59:01,383][88175] Fps is (10 sec: 16793.7, 60 sec: 12424.5, 300 sec: 12413.0). Total num frames: 32378880. Throughput: 0: 3165.6. Samples: 7090396. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:59:01,384][88175] Avg episode reward: [(0, '11.470')]
+[2023-09-11 21:59:02,458][83013] Updated weights for policy 0, policy_version 7910 (0.0008)
+[2023-09-11 21:59:04,927][83013] Updated weights for policy 0, policy_version 7920 (0.0008)
+[2023-09-11 21:59:06,384][88175] Fps is (10 sec: 16793.6, 60 sec: 12902.4, 300 sec: 12510.1). Total num frames: 32464896. Throughput: 0: 3251.4. Samples: 7102958. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:59:06,385][88175] Avg episode reward: [(0, '11.750')]
+[2023-09-11 21:59:07,372][83013] Updated weights for policy 0, policy_version 7930 (0.0008)
+[2023-09-11 21:59:09,830][83013] Updated weights for policy 0, policy_version 7940 (0.0008)
+[2023-09-11 21:59:11,383][88175] Fps is (10 sec: 16793.5, 60 sec: 13312.0, 300 sec: 12593.5). Total num frames: 32546816. Throughput: 0: 3428.9. Samples: 7128006. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:59:11,385][88175] Avg episode reward: [(0, '11.870')]
+[2023-09-11 21:59:12,364][83013] Updated weights for policy 0, policy_version 7950 (0.0008)
+[2023-09-11 21:59:14,772][83013] Updated weights for policy 0, policy_version 7960 (0.0008)
+[2023-09-11 21:59:16,383][88175] Fps is (10 sec: 16384.2, 60 sec: 13721.6, 300 sec: 12676.8). Total num frames: 32628736. Throughput: 0: 3603.3. Samples: 7153088. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:59:16,385][88175] Avg episode reward: [(0, '11.620')]
+[2023-09-11 21:59:17,231][83013] Updated weights for policy 0, policy_version 7970 (0.0009)
+[2023-09-11 21:59:19,675][83013] Updated weights for policy 0, policy_version 7980 (0.0009)
+[2023-09-11 21:59:21,383][88175] Fps is (10 sec: 16383.9, 60 sec: 14131.2, 300 sec: 12760.1). Total num frames: 32710656. Throughput: 0: 3685.1. Samples: 7165458. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:59:21,385][88175] Avg episode reward: [(0, '11.900')]
+[2023-09-11 21:59:22,111][83013] Updated weights for policy 0, policy_version 7990 (0.0008)
+[2023-09-11 21:59:24,573][83013] Updated weights for policy 0, policy_version 8000 (0.0009)
+[2023-09-11 21:59:26,383][88175] Fps is (10 sec: 15974.6, 60 sec: 14472.6, 300 sec: 12829.5). Total num frames: 32788480. Throughput: 0: 3850.3. Samples: 7190266. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:59:26,384][88175] Avg episode reward: [(0, '11.660')]
+[2023-09-11 21:59:28,008][83013] Updated weights for policy 0, policy_version 8010 (0.0009)
+[2023-09-11 21:59:31,383][88175] Fps is (10 sec: 13516.9, 60 sec: 14472.6, 300 sec: 12829.5). Total num frames: 32845824. Throughput: 0: 3844.5. Samples: 7207648. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 21:59:31,385][88175] Avg episode reward: [(0, '11.700')]
+[2023-09-11 21:59:31,685][83013] Updated weights for policy 0, policy_version 8020 (0.0009)
+[2023-09-11 21:59:35,265][83013] Updated weights for policy 0, policy_version 8030 (0.0009)
+[2023-09-11 21:59:36,384][88175] Fps is (10 sec: 11468.4, 60 sec: 14472.5, 300 sec: 12829.5). Total num frames: 32903168. Throughput: 0: 3841.4. Samples: 7216116. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:59:36,385][88175] Avg episode reward: [(0, '11.710')]
+[2023-09-11 21:59:38,798][83013] Updated weights for policy 0, policy_version 8040 (0.0009)
+[2023-09-11 21:59:41,383][88175] Fps is (10 sec: 11468.7, 60 sec: 14404.2, 300 sec: 12829.5). Total num frames: 32960512. Throughput: 0: 3735.9. Samples: 7233372. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:59:41,385][88175] Avg episode reward: [(0, '11.620')]
+[2023-09-11 21:59:42,333][83013] Updated weights for policy 0, policy_version 8050 (0.0009)
+[2023-09-11 21:59:45,857][83013] Updated weights for policy 0, policy_version 8060 (0.0008)
+[2023-09-11 21:59:46,384][88175] Fps is (10 sec: 11468.8, 60 sec: 14404.2, 300 sec: 12829.5). Total num frames: 33017856. Throughput: 0: 3562.8. Samples: 7250722. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:59:46,385][88175] Avg episode reward: [(0, '12.070')]
+[2023-09-11 21:59:49,440][83013] Updated weights for policy 0, policy_version 8070 (0.0008)
+[2023-09-11 21:59:51,383][88175] Fps is (10 sec: 11468.8, 60 sec: 14404.2, 300 sec: 12829.5). Total num frames: 33075200. Throughput: 0: 3478.5. Samples: 7259488. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 21:59:51,385][88175] Avg episode reward: [(0, '11.990')]
+[2023-09-11 21:59:52,986][83013] Updated weights for policy 0, policy_version 8080 (0.0009)
+[2023-09-11 21:59:56,383][88175] Fps is (10 sec: 11469.1, 60 sec: 13926.4, 300 sec: 12829.5). Total num frames: 33132544. Throughput: 0: 3298.8. Samples: 7276454. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 21:59:56,385][88175] Avg episode reward: [(0, '11.580')]
+[2023-09-11 21:59:56,633][83013] Updated weights for policy 0, policy_version 8090 (0.0009)
+[2023-09-11 22:00:00,203][83013] Updated weights for policy 0, policy_version 8100 (0.0008)
+[2023-09-11 22:00:01,383][88175] Fps is (10 sec: 11468.9, 60 sec: 13516.8, 300 sec: 12829.5). Total num frames: 33189888. Throughput: 0: 3122.8. Samples: 7293614. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 22:00:01,385][88175] Avg episode reward: [(0, '11.470')]
+[2023-09-11 22:00:03,834][83013] Updated weights for policy 0, policy_version 8110 (0.0009)
+[2023-09-11 22:00:06,383][88175] Fps is (10 sec: 11468.8, 60 sec: 13039.0, 300 sec: 12829.5). Total num frames: 33247232. Throughput: 0: 3034.0. Samples: 7301986. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:00:06,385][88175] Avg episode reward: [(0, '11.800')]
+[2023-09-11 22:00:06,392][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000008117_33247232.pth...
+[2023-09-11 22:00:06,443][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000007400_30310400.pth
+[2023-09-11 22:00:07,396][83013] Updated weights for policy 0, policy_version 8120 (0.0009)
+[2023-09-11 22:00:10,994][83013] Updated weights for policy 0, policy_version 8130 (0.0008)
+[2023-09-11 22:00:11,384][88175] Fps is (10 sec: 11468.6, 60 sec: 12629.3, 300 sec: 12829.5). Total num frames: 33304576. Throughput: 0: 2867.0. Samples: 7319280. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:00:11,385][88175] Avg episode reward: [(0, '11.890')]
+[2023-09-11 22:00:14,538][83013] Updated weights for policy 0, policy_version 8140 (0.0008)
+[2023-09-11 22:00:16,383][88175] Fps is (10 sec: 11468.7, 60 sec: 12219.7, 300 sec: 12829.5). Total num frames: 33361920. Throughput: 0: 2863.1. Samples: 7336486. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:00:16,385][88175] Avg episode reward: [(0, '11.530')]
+[2023-09-11 22:00:18,072][83013] Updated weights for policy 0, policy_version 8150 (0.0009)
+[2023-09-11 22:00:21,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11810.1, 300 sec: 12760.1). Total num frames: 33419264. Throughput: 0: 2865.3. Samples: 7345054. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:00:21,385][88175] Avg episode reward: [(0, '11.950')]
+[2023-09-11 22:00:21,679][83013] Updated weights for policy 0, policy_version 8160 (0.0010)
+[2023-09-11 22:00:25,238][83013] Updated weights for policy 0, policy_version 8170 (0.0009)
+[2023-09-11 22:00:26,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12676.8). Total num frames: 33476608. Throughput: 0: 2865.2. Samples: 7362308. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:00:26,385][88175] Avg episode reward: [(0, '11.820')]
+[2023-09-11 22:00:28,736][83013] Updated weights for policy 0, policy_version 8180 (0.0009)
+[2023-09-11 22:00:31,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12579.6). Total num frames: 33533952. Throughput: 0: 2860.3. Samples: 7379434. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 22:00:31,385][88175] Avg episode reward: [(0, '11.770')]
+[2023-09-11 22:00:32,363][83013] Updated weights for policy 0, policy_version 8190 (0.0008)
+[2023-09-11 22:00:35,906][83013] Updated weights for policy 0, policy_version 8200 (0.0011)
+[2023-09-11 22:00:36,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12496.3). Total num frames: 33591296. Throughput: 0: 2854.2. Samples: 7387926. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 22:00:36,385][88175] Avg episode reward: [(0, '11.840')]
+[2023-09-11 22:00:39,386][83013] Updated weights for policy 0, policy_version 8210 (0.0011)
+[2023-09-11 22:00:41,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12413.0). Total num frames: 33648640. Throughput: 0: 2871.4. Samples: 7405668. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 22:00:41,384][88175] Avg episode reward: [(0, '11.700')]
+[2023-09-11 22:00:42,918][83013] Updated weights for policy 0, policy_version 8220 (0.0009)
+[2023-09-11 22:00:46,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12329.6). Total num frames: 33705984. Throughput: 0: 2878.7. Samples: 7423158. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:00:46,385][88175] Avg episode reward: [(0, '11.870')]
+[2023-09-11 22:00:46,426][83013] Updated weights for policy 0, policy_version 8230 (0.0010)
+[2023-09-11 22:00:49,961][83013] Updated weights for policy 0, policy_version 8240 (0.0008)
+[2023-09-11 22:00:51,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11468.8, 300 sec: 12232.5). Total num frames: 33763328. Throughput: 0: 2887.9. Samples: 7431940. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:00:51,385][88175] Avg episode reward: [(0, '11.840')]
+[2023-09-11 22:00:53,534][83013] Updated weights for policy 0, policy_version 8250 (0.0008)
+[2023-09-11 22:00:56,384][88175] Fps is (10 sec: 11878.4, 60 sec: 11537.0, 300 sec: 12163.0). Total num frames: 33824768. Throughput: 0: 2887.0. Samples: 7449194. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:00:56,385][88175] Avg episode reward: [(0, '11.640')]
+[2023-09-11 22:00:57,075][83013] Updated weights for policy 0, policy_version 8260 (0.0008)
+[2023-09-11 22:01:00,564][83013] Updated weights for policy 0, policy_version 8270 (0.0009)
+[2023-09-11 22:01:01,383][88175] Fps is (10 sec: 11878.7, 60 sec: 11537.1, 300 sec: 12107.5). Total num frames: 33882112. Throughput: 0: 2889.6. Samples: 7466516. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:01:01,385][88175] Avg episode reward: [(0, '11.810')]
+[2023-09-11 22:01:04,249][83013] Updated weights for policy 0, policy_version 8280 (0.0009)
+[2023-09-11 22:01:06,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.0, 300 sec: 12107.5). Total num frames: 33939456. Throughput: 0: 2887.8. Samples: 7475006. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:01:06,385][88175] Avg episode reward: [(0, '11.750')]
+[2023-09-11 22:01:07,755][83013] Updated weights for policy 0, policy_version 8290 (0.0008)
+[2023-09-11 22:01:11,326][83013] Updated weights for policy 0, policy_version 8300 (0.0009)
+[2023-09-11 22:01:11,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12107.5). Total num frames: 33996800. Throughput: 0: 2887.1. Samples: 7492228. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:01:11,385][88175] Avg episode reward: [(0, '11.830')]
+[2023-09-11 22:01:14,881][83013] Updated weights for policy 0, policy_version 8310 (0.0009)
+[2023-09-11 22:01:16,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11537.1, 300 sec: 12107.5). Total num frames: 34054144. Throughput: 0: 2891.1. Samples: 7509532. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:01:16,384][88175] Avg episode reward: [(0, '11.810')]
+[2023-09-11 22:01:18,439][83013] Updated weights for policy 0, policy_version 8320 (0.0009)
+[2023-09-11 22:01:21,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.0, 300 sec: 12107.5). Total num frames: 34111488. Throughput: 0: 2894.2. Samples: 7518166. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:01:21,385][88175] Avg episode reward: [(0, '11.570')]
+[2023-09-11 22:01:21,999][83013] Updated weights for policy 0, policy_version 8330 (0.0009)
+[2023-09-11 22:01:25,523][83013] Updated weights for policy 0, policy_version 8340 (0.0009)
+[2023-09-11 22:01:26,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12107.5). Total num frames: 34168832. Throughput: 0: 2883.7. Samples: 7535434. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:01:26,385][88175] Avg episode reward: [(0, '11.480')]
+[2023-09-11 22:01:29,146][83013] Updated weights for policy 0, policy_version 8350 (0.0009)
+[2023-09-11 22:01:31,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11537.1, 300 sec: 12107.5). Total num frames: 34226176. Throughput: 0: 2879.7. Samples: 7552744. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:01:31,385][88175] Avg episode reward: [(0, '11.770')]
+[2023-09-11 22:01:32,658][83013] Updated weights for policy 0, policy_version 8360 (0.0008)
+[2023-09-11 22:01:36,246][83013] Updated weights for policy 0, policy_version 8370 (0.0008)
+[2023-09-11 22:01:36,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11537.0, 300 sec: 12107.5). Total num frames: 34283520. Throughput: 0: 2874.5. Samples: 7561292. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 22:01:36,386][88175] Avg episode reward: [(0, '11.810')]
+[2023-09-11 22:01:39,809][83013] Updated weights for policy 0, policy_version 8380 (0.0009)
+[2023-09-11 22:01:41,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.0, 300 sec: 12107.5). Total num frames: 34340864. Throughput: 0: 2871.4. Samples: 7578408. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:01:41,385][88175] Avg episode reward: [(0, '11.690')]
+[2023-09-11 22:01:43,408][83013] Updated weights for policy 0, policy_version 8390 (0.0009)
+[2023-09-11 22:01:46,384][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12107.5). Total num frames: 34398208. Throughput: 0: 2871.4. Samples: 7595730. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:01:46,386][88175] Avg episode reward: [(0, '11.900')]
+[2023-09-11 22:01:46,960][83013] Updated weights for policy 0, policy_version 8400 (0.0009)
+[2023-09-11 22:01:50,531][83013] Updated weights for policy 0, policy_version 8410 (0.0008)
+[2023-09-11 22:01:51,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12107.5). Total num frames: 34455552. Throughput: 0: 2876.1. Samples: 7604428. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:01:51,384][88175] Avg episode reward: [(0, '11.830')]
+[2023-09-11 22:01:54,079][83013] Updated weights for policy 0, policy_version 8420 (0.0008)
+[2023-09-11 22:01:56,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 34512896. Throughput: 0: 2874.3. Samples: 7621574. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:01:56,386][88175] Avg episode reward: [(0, '12.080')]
+[2023-09-11 22:01:57,636][83013] Updated weights for policy 0, policy_version 8430 (0.0009)
+[2023-09-11 22:02:01,238][83013] Updated weights for policy 0, policy_version 8440 (0.0009)
+[2023-09-11 22:02:01,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 34570240. Throughput: 0: 2872.0. Samples: 7638770. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:02:01,385][88175] Avg episode reward: [(0, '11.920')]
+[2023-09-11 22:02:04,829][83013] Updated weights for policy 0, policy_version 8450 (0.0009)
+[2023-09-11 22:02:06,384][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 34627584. Throughput: 0: 2869.6. Samples: 7647296. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:02:06,385][88175] Avg episode reward: [(0, '11.980')]
+[2023-09-11 22:02:06,391][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000008454_34627584.pth...
+[2023-09-11 22:02:06,442][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000007737_31690752.pth
+[2023-09-11 22:02:08,401][83013] Updated weights for policy 0, policy_version 8460 (0.0009)
+[2023-09-11 22:02:11,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12107.5). Total num frames: 34684928. Throughput: 0: 2863.9. Samples: 7664310. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:02:11,384][88175] Avg episode reward: [(0, '11.760')]
+[2023-09-11 22:02:11,834][83013] Updated weights for policy 0, policy_version 8470 (0.0008)
+[2023-09-11 22:02:14,342][83013] Updated weights for policy 0, policy_version 8480 (0.0008)
+[2023-09-11 22:02:16,384][88175] Fps is (10 sec: 13516.7, 60 sec: 11810.1, 300 sec: 12176.9). Total num frames: 34762752. Throughput: 0: 2980.6. Samples: 7686870. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:02:16,385][88175] Avg episode reward: [(0, '12.030')]
+[2023-09-11 22:02:16,911][83013] Updated weights for policy 0, policy_version 8490 (0.0009)
+[2023-09-11 22:02:19,429][83013] Updated weights for policy 0, policy_version 8500 (0.0008)
+[2023-09-11 22:02:21,383][88175] Fps is (10 sec: 15974.4, 60 sec: 12219.8, 300 sec: 12260.2). Total num frames: 34844672. Throughput: 0: 3064.2. Samples: 7699180. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 22:02:21,385][88175] Avg episode reward: [(0, '11.990')]
+[2023-09-11 22:02:21,880][83013] Updated weights for policy 0, policy_version 8510 (0.0008)
+[2023-09-11 22:02:24,343][83013] Updated weights for policy 0, policy_version 8520 (0.0008)
+[2023-09-11 22:02:26,383][88175] Fps is (10 sec: 16794.0, 60 sec: 12697.6, 300 sec: 12357.4). Total num frames: 34930688. Throughput: 0: 3233.6. Samples: 7723918. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:02:26,385][88175] Avg episode reward: [(0, '12.280')]
+[2023-09-11 22:02:26,772][83013] Updated weights for policy 0, policy_version 8530 (0.0008)
+[2023-09-11 22:02:29,271][83013] Updated weights for policy 0, policy_version 8540 (0.0009)
+[2023-09-11 22:02:31,383][88175] Fps is (10 sec: 16793.6, 60 sec: 13107.2, 300 sec: 12440.7). Total num frames: 35012608. Throughput: 0: 3403.0. Samples: 7748862. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 22:02:31,384][88175] Avg episode reward: [(0, '11.690')]
+[2023-09-11 22:02:31,779][83013] Updated weights for policy 0, policy_version 8550 (0.0009)
+[2023-09-11 22:02:34,276][83013] Updated weights for policy 0, policy_version 8560 (0.0008)
+[2023-09-11 22:02:36,383][88175] Fps is (10 sec: 16384.1, 60 sec: 13516.9, 300 sec: 12524.1). Total num frames: 35094528. Throughput: 0: 3484.3. Samples: 7761222. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 22:02:36,384][88175] Avg episode reward: [(0, '12.020')]
+[2023-09-11 22:02:36,730][83013] Updated weights for policy 0, policy_version 8570 (0.0008)
+[2023-09-11 22:02:39,206][83013] Updated weights for policy 0, policy_version 8580 (0.0008)
+[2023-09-11 22:02:41,383][88175] Fps is (10 sec: 16383.8, 60 sec: 13926.4, 300 sec: 12607.3). Total num frames: 35176448. Throughput: 0: 3654.6. Samples: 7786028. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:02:41,385][88175] Avg episode reward: [(0, '12.080')]
+[2023-09-11 22:02:41,673][83013] Updated weights for policy 0, policy_version 8590 (0.0008)
+[2023-09-11 22:02:44,178][83013] Updated weights for policy 0, policy_version 8600 (0.0009)
+[2023-09-11 22:02:46,383][88175] Fps is (10 sec: 16793.6, 60 sec: 14404.3, 300 sec: 12690.7). Total num frames: 35262464. Throughput: 0: 3817.7. Samples: 7810566. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 22:02:46,385][88175] Avg episode reward: [(0, '12.030')]
+[2023-09-11 22:02:46,626][83013] Updated weights for policy 0, policy_version 8610 (0.0009)
+[2023-09-11 22:02:49,217][83013] Updated weights for policy 0, policy_version 8620 (0.0009)
+[2023-09-11 22:02:51,383][88175] Fps is (10 sec: 15155.3, 60 sec: 14540.8, 300 sec: 12718.4). Total num frames: 35328000. Throughput: 0: 3902.8. Samples: 7822922. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 22:02:51,385][88175] Avg episode reward: [(0, '12.150')]
+[2023-09-11 22:02:52,830][83013] Updated weights for policy 0, policy_version 8630 (0.0008)
+[2023-09-11 22:02:56,365][83013] Updated weights for policy 0, policy_version 8640 (0.0009)
+[2023-09-11 22:02:56,383][88175] Fps is (10 sec: 12697.4, 60 sec: 14609.1, 300 sec: 12732.3). Total num frames: 35389440. Throughput: 0: 3912.8. Samples: 7840386. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:02:56,385][88175] Avg episode reward: [(0, '11.960')]
+[2023-09-11 22:02:59,891][83013] Updated weights for policy 0, policy_version 8650 (0.0009)
+[2023-09-11 22:03:01,383][88175] Fps is (10 sec: 11878.5, 60 sec: 14609.1, 300 sec: 12732.3). Total num frames: 35446784. Throughput: 0: 3795.1. Samples: 7857650. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:03:01,385][88175] Avg episode reward: [(0, '11.760')]
+[2023-09-11 22:03:03,524][83013] Updated weights for policy 0, policy_version 8660 (0.0009)
+[2023-09-11 22:03:06,384][88175] Fps is (10 sec: 11468.7, 60 sec: 14609.1, 300 sec: 12732.3). Total num frames: 35504128. Throughput: 0: 3711.6. Samples: 7866202. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:03:06,385][88175] Avg episode reward: [(0, '11.990')]
+[2023-09-11 22:03:07,007][83013] Updated weights for policy 0, policy_version 8670 (0.0008)
+[2023-09-11 22:03:10,488][83013] Updated weights for policy 0, policy_version 8680 (0.0008)
+[2023-09-11 22:03:11,383][88175] Fps is (10 sec: 11468.8, 60 sec: 14609.1, 300 sec: 12732.3). Total num frames: 35561472. Throughput: 0: 3553.2. Samples: 7883810. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:03:11,385][88175] Avg episode reward: [(0, '11.840')]
+[2023-09-11 22:03:14,077][83013] Updated weights for policy 0, policy_version 8690 (0.0008)
+[2023-09-11 22:03:16,383][88175] Fps is (10 sec: 11468.8, 60 sec: 14267.8, 300 sec: 12732.3). Total num frames: 35618816. Throughput: 0: 3380.8. Samples: 7900998. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:03:16,386][88175] Avg episode reward: [(0, '11.800')]
+[2023-09-11 22:03:17,556][83013] Updated weights for policy 0, policy_version 8700 (0.0008)
+[2023-09-11 22:03:21,180][83013] Updated weights for policy 0, policy_version 8710 (0.0009)
+[2023-09-11 22:03:21,383][88175] Fps is (10 sec: 11468.7, 60 sec: 13858.1, 300 sec: 12732.3). Total num frames: 35676160. Throughput: 0: 3300.0. Samples: 7909722. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:03:21,385][88175] Avg episode reward: [(0, '11.810')]
+[2023-09-11 22:03:24,717][83013] Updated weights for policy 0, policy_version 8720 (0.0008)
+[2023-09-11 22:03:26,383][88175] Fps is (10 sec: 11468.9, 60 sec: 13380.3, 300 sec: 12732.3). Total num frames: 35733504. Throughput: 0: 3132.9. Samples: 7927008. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:03:26,384][88175] Avg episode reward: [(0, '11.980')]
+[2023-09-11 22:03:28,333][83013] Updated weights for policy 0, policy_version 8730 (0.0009)
+[2023-09-11 22:03:31,383][88175] Fps is (10 sec: 11468.9, 60 sec: 12970.7, 300 sec: 12732.3). Total num frames: 35790848. Throughput: 0: 2966.5. Samples: 7944058. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:03:31,384][88175] Avg episode reward: [(0, '12.180')]
+[2023-09-11 22:03:31,896][83013] Updated weights for policy 0, policy_version 8740 (0.0008)
+[2023-09-11 22:03:35,494][83013] Updated weights for policy 0, policy_version 8750 (0.0008)
+[2023-09-11 22:03:36,383][88175] Fps is (10 sec: 11468.7, 60 sec: 12561.0, 300 sec: 12718.4). Total num frames: 35848192. Throughput: 0: 2882.1. Samples: 7952618. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:03:36,385][88175] Avg episode reward: [(0, '12.100')]
+[2023-09-11 22:03:39,045][83013] Updated weights for policy 0, policy_version 8760 (0.0009)
+[2023-09-11 22:03:41,383][88175] Fps is (10 sec: 11468.7, 60 sec: 12151.5, 300 sec: 12718.4). Total num frames: 35905536. Throughput: 0: 2881.6. Samples: 7970056. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:03:41,385][88175] Avg episode reward: [(0, '12.220')]
+[2023-09-11 22:03:42,555][83013] Updated weights for policy 0, policy_version 8770 (0.0008)
+[2023-09-11 22:03:46,101][83013] Updated weights for policy 0, policy_version 8780 (0.0009)
+[2023-09-11 22:03:46,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11673.6, 300 sec: 12718.4). Total num frames: 35962880. Throughput: 0: 2880.9. Samples: 7987292. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:03:46,385][88175] Avg episode reward: [(0, '12.020')]
+[2023-09-11 22:03:49,535][83013] Updated weights for policy 0, policy_version 8790 (0.0008)
+[2023-09-11 22:03:51,383][88175] Fps is (10 sec: 11878.5, 60 sec: 11605.3, 300 sec: 12635.1). Total num frames: 36024320. Throughput: 0: 2886.2. Samples: 7996082. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:03:51,384][88175] Avg episode reward: [(0, '12.260')]
+[2023-09-11 22:03:53,110][83013] Updated weights for policy 0, policy_version 8800 (0.0008)
+[2023-09-11 22:03:56,384][88175] Fps is (10 sec: 11878.3, 60 sec: 11537.0, 300 sec: 12551.8). Total num frames: 36081664. Throughput: 0: 2882.2. Samples: 8013510. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
+[2023-09-11 22:03:56,385][88175] Avg episode reward: [(0, '12.120')]
+[2023-09-11 22:03:56,740][83013] Updated weights for policy 0, policy_version 8810 (0.0008)
+[2023-09-11 22:04:00,345][83013] Updated weights for policy 0, policy_version 8820 (0.0008)
+[2023-09-11 22:04:01,384][88175] Fps is (10 sec: 11059.0, 60 sec: 11468.8, 300 sec: 12440.7). Total num frames: 36134912. Throughput: 0: 2878.4. Samples: 8030526. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:04:01,386][88175] Avg episode reward: [(0, '11.940')]
+[2023-09-11 22:04:03,846][83013] Updated weights for policy 0, policy_version 8830 (0.0009)
+[2023-09-11 22:04:06,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11537.1, 300 sec: 12371.3). Total num frames: 36196352. Throughput: 0: 2878.1. Samples: 8039236. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:04:06,386][88175] Avg episode reward: [(0, '12.040')]
+[2023-09-11 22:04:06,392][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000008837_36196352.pth...
+[2023-09-11 22:04:06,442][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000008117_33247232.pth
+[2023-09-11 22:04:07,356][83013] Updated weights for policy 0, policy_version 8840 (0.0008)
+[2023-09-11 22:04:10,894][83013] Updated weights for policy 0, policy_version 8850 (0.0009)
+[2023-09-11 22:04:11,383][88175] Fps is (10 sec: 11878.7, 60 sec: 11537.1, 300 sec: 12288.0). Total num frames: 36253696. Throughput: 0: 2881.6. Samples: 8056680. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:04:11,384][88175] Avg episode reward: [(0, '12.040')]
+[2023-09-11 22:04:14,516][83013] Updated weights for policy 0, policy_version 8860 (0.0008)
+[2023-09-11 22:04:16,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.1, 300 sec: 12204.7). Total num frames: 36311040. Throughput: 0: 2885.3. Samples: 8073898. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 22:04:16,384][88175] Avg episode reward: [(0, '12.090')]
+[2023-09-11 22:04:18,005][83013] Updated weights for policy 0, policy_version 8870 (0.0009)
+[2023-09-11 22:04:21,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.1, 300 sec: 12135.3). Total num frames: 36368384. Throughput: 0: 2893.2. Samples: 8082812. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 22:04:21,385][88175] Avg episode reward: [(0, '12.050')]
+[2023-09-11 22:04:21,496][83013] Updated weights for policy 0, policy_version 8880 (0.0008)
+[2023-09-11 22:04:25,027][83013] Updated weights for policy 0, policy_version 8890 (0.0009)
+[2023-09-11 22:04:26,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11537.0, 300 sec: 12135.3). Total num frames: 36425728. Throughput: 0: 2891.5. Samples: 8100172. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:04:26,385][88175] Avg episode reward: [(0, '12.050')]
+[2023-09-11 22:04:28,601][83013] Updated weights for policy 0, policy_version 8900 (0.0008)
+[2023-09-11 22:04:31,384][88175] Fps is (10 sec: 11878.3, 60 sec: 11605.3, 300 sec: 12149.2). Total num frames: 36487168. Throughput: 0: 2896.1. Samples: 8117618. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 22:04:31,385][88175] Avg episode reward: [(0, '11.950')]
+[2023-09-11 22:04:32,117][83013] Updated weights for policy 0, policy_version 8910 (0.0009)
+[2023-09-11 22:04:35,682][83013] Updated weights for policy 0, policy_version 8920 (0.0009)
+[2023-09-11 22:04:36,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.0, 300 sec: 12135.3). Total num frames: 36540416. Throughput: 0: 2890.2. Samples: 8126140. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 22:04:36,385][88175] Avg episode reward: [(0, '11.930')]
+[2023-09-11 22:04:39,250][83013] Updated weights for policy 0, policy_version 8930 (0.0009)
+[2023-09-11 22:04:41,384][88175] Fps is (10 sec: 11059.0, 60 sec: 11537.0, 300 sec: 12135.3). Total num frames: 36597760. Throughput: 0: 2885.4. Samples: 8143352. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:04:41,386][88175] Avg episode reward: [(0, '11.960')]
+[2023-09-11 22:04:42,884][83013] Updated weights for policy 0, policy_version 8940 (0.0008)
+[2023-09-11 22:04:46,384][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12135.3). Total num frames: 36655104. Throughput: 0: 2888.0. Samples: 8160486. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:04:46,385][88175] Avg episode reward: [(0, '12.030')]
+[2023-09-11 22:04:46,456][83013] Updated weights for policy 0, policy_version 8950 (0.0008)
+[2023-09-11 22:04:50,010][83013] Updated weights for policy 0, policy_version 8960 (0.0009)
+[2023-09-11 22:04:51,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 36712448. Throughput: 0: 2887.5. Samples: 8169172. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:04:51,385][88175] Avg episode reward: [(0, '11.920')]
+[2023-09-11 22:04:53,492][83013] Updated weights for policy 0, policy_version 8970 (0.0009)
+[2023-09-11 22:04:56,384][88175] Fps is (10 sec: 11878.4, 60 sec: 11537.1, 300 sec: 12149.1). Total num frames: 36773888. Throughput: 0: 2884.7. Samples: 8186490. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:04:56,386][88175] Avg episode reward: [(0, '12.020')]
+[2023-09-11 22:04:57,086][83013] Updated weights for policy 0, policy_version 8980 (0.0008)
+[2023-09-11 22:05:00,610][83013] Updated weights for policy 0, policy_version 8990 (0.0008)
+[2023-09-11 22:05:01,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11605.4, 300 sec: 12149.2). Total num frames: 36831232. Throughput: 0: 2888.6. Samples: 8203886. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
+[2023-09-11 22:05:01,385][88175] Avg episode reward: [(0, '12.000')]
+[2023-09-11 22:05:04,121][83013] Updated weights for policy 0, policy_version 9000 (0.0008)
+[2023-09-11 22:05:06,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11537.1, 300 sec: 12149.2). Total num frames: 36888576. Throughput: 0: 2882.8. Samples: 8212538. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:05:06,384][88175] Avg episode reward: [(0, '11.980')]
+[2023-09-11 22:05:07,712][83013] Updated weights for policy 0, policy_version 9010 (0.0008)
+[2023-09-11 22:05:11,248][83013] Updated weights for policy 0, policy_version 9020 (0.0009)
+[2023-09-11 22:05:11,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.0, 300 sec: 12149.2). Total num frames: 36945920. Throughput: 0: 2881.2. Samples: 8229826. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:05:11,385][88175] Avg episode reward: [(0, '11.960')]
+[2023-09-11 22:05:14,809][83013] Updated weights for policy 0, policy_version 9030 (0.0008)
+[2023-09-11 22:05:16,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11537.0, 300 sec: 12149.1). Total num frames: 37003264. Throughput: 0: 2875.8. Samples: 8247028. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:05:16,386][88175] Avg episode reward: [(0, '12.090')]
+[2023-09-11 22:05:18,423][83013] Updated weights for policy 0, policy_version 9040 (0.0008)
+[2023-09-11 22:05:21,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.0, 300 sec: 12149.1). Total num frames: 37060608. Throughput: 0: 2877.4. Samples: 8255624. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 22:05:21,385][88175] Avg episode reward: [(0, '12.250')]
+[2023-09-11 22:05:21,851][83013] Updated weights for policy 0, policy_version 9050 (0.0009)
+[2023-09-11 22:05:25,482][83013] Updated weights for policy 0, policy_version 9060 (0.0010)
+[2023-09-11 22:05:26,383][88175] Fps is (10 sec: 11469.1, 60 sec: 11537.1, 300 sec: 12149.2). Total num frames: 37117952. Throughput: 0: 2880.4. Samples: 8272968. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
+[2023-09-11 22:05:26,385][88175] Avg episode reward: [(0, '12.160')]
+[2023-09-11 22:05:29,004][83013] Updated weights for policy 0, policy_version 9070 (0.0009)
+[2023-09-11 22:05:31,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12149.2). Total num frames: 37175296. Throughput: 0: 2885.0. Samples: 8290312. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:05:31,385][88175] Avg episode reward: [(0, '12.030')]
+[2023-09-11 22:05:32,540][83013] Updated weights for policy 0, policy_version 9080 (0.0008)
+[2023-09-11 22:05:35,883][83013] Updated weights for policy 0, policy_version 9090 (0.0009)
+[2023-09-11 22:05:36,384][88175] Fps is (10 sec: 12287.8, 60 sec: 11673.6, 300 sec: 12176.9). Total num frames: 37240832. Throughput: 0: 2884.7. Samples: 8298984. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:05:36,385][88175] Avg episode reward: [(0, '11.910')]
+[2023-09-11 22:05:38,341][83013] Updated weights for policy 0, policy_version 9100 (0.0008)
+[2023-09-11 22:05:40,785][83013] Updated weights for policy 0, policy_version 9110 (0.0008)
+[2023-09-11 22:05:41,383][88175] Fps is (10 sec: 14745.7, 60 sec: 12083.3, 300 sec: 12260.2). Total num frames: 37322752. Throughput: 0: 3014.9. Samples: 8322158. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:05:41,384][88175] Avg episode reward: [(0, '12.010')]
+[2023-09-11 22:05:43,257][83013] Updated weights for policy 0, policy_version 9120 (0.0008)
+[2023-09-11 22:05:45,674][83013] Updated weights for policy 0, policy_version 9130 (0.0009)
+[2023-09-11 22:05:46,384][88175] Fps is (10 sec: 16384.0, 60 sec: 12492.8, 300 sec: 12343.5). Total num frames: 37404672. Throughput: 0: 3181.4. Samples: 8347048. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
+[2023-09-11 22:05:46,385][88175] Avg episode reward: [(0, '12.160')]
+[2023-09-11 22:05:48,140][83013] Updated weights for policy 0, policy_version 9140 (0.0008)
+[2023-09-11 22:05:50,620][83013] Updated weights for policy 0, policy_version 9150 (0.0009)
+[2023-09-11 22:05:51,384][88175] Fps is (10 sec: 16793.3, 60 sec: 12970.6, 300 sec: 12426.9). Total num frames: 37490688. Throughput: 0: 3266.8. Samples: 8359544. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:05:51,385][88175] Avg episode reward: [(0, '12.090')]
+[2023-09-11 22:05:53,102][83013] Updated weights for policy 0, policy_version 9160 (0.0009)
+[2023-09-11 22:05:55,638][83013] Updated weights for policy 0, policy_version 9170 (0.0008)
+[2023-09-11 22:05:56,383][88175] Fps is (10 sec: 16793.8, 60 sec: 13312.0, 300 sec: 12510.2). Total num frames: 37572608. Throughput: 0: 3430.5. Samples: 8384198. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:05:56,385][88175] Avg episode reward: [(0, '12.040')]
+[2023-09-11 22:05:58,083][83013] Updated weights for policy 0, policy_version 9180 (0.0008)
+[2023-09-11 22:06:00,522][83013] Updated weights for policy 0, policy_version 9190 (0.0008)
+[2023-09-11 22:06:01,383][88175] Fps is (10 sec: 16384.3, 60 sec: 13721.6, 300 sec: 12593.5). Total num frames: 37654528. Throughput: 0: 3601.9. Samples: 8409110. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:06:01,384][88175] Avg episode reward: [(0, '12.170')]
+[2023-09-11 22:06:02,978][83013] Updated weights for policy 0, policy_version 9200 (0.0008)
+[2023-09-11 22:06:05,452][83013] Updated weights for policy 0, policy_version 9210 (0.0008)
+[2023-09-11 22:06:06,383][88175] Fps is (10 sec: 16384.0, 60 sec: 14131.2, 300 sec: 12676.8). Total num frames: 37736448. Throughput: 0: 3695.6. Samples: 8421926. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:06:06,385][88175] Avg episode reward: [(0, '12.110')]
+[2023-09-11 22:06:06,435][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009214_37740544.pth...
+[2023-09-11 22:06:06,482][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000008454_34627584.pth
+[2023-09-11 22:06:07,885][83013] Updated weights for policy 0, policy_version 9220 (0.0008)
+[2023-09-11 22:06:10,363][83013] Updated weights for policy 0, policy_version 9230 (0.0008)
+[2023-09-11 22:06:11,383][88175] Fps is (10 sec: 16383.9, 60 sec: 14540.8, 300 sec: 12760.1). Total num frames: 37818368. Throughput: 0: 3861.1. Samples: 8446716. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:06:11,385][88175] Avg episode reward: [(0, '12.220')]
+[2023-09-11 22:06:13,545][83013] Updated weights for policy 0, policy_version 9240 (0.0009)
+[2023-09-11 22:06:16,384][88175] Fps is (10 sec: 14335.9, 60 sec: 14609.1, 300 sec: 12774.0). Total num frames: 37879808. Throughput: 0: 3899.5. Samples: 8465788. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:06:16,385][88175] Avg episode reward: [(0, '12.160')]
+[2023-09-11 22:06:17,089][83013] Updated weights for policy 0, policy_version 9250 (0.0008)
+[2023-09-11 22:06:20,645][83013] Updated weights for policy 0, policy_version 9260 (0.0008)
+[2023-09-11 22:06:21,384][88175] Fps is (10 sec: 11878.2, 60 sec: 14609.1, 300 sec: 12774.0). Total num frames: 37937152. Throughput: 0: 3896.0. Samples: 8474304. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:06:21,385][88175] Avg episode reward: [(0, '12.210')]
+[2023-09-11 22:06:24,222][83013] Updated weights for policy 0, policy_version 9270 (0.0008)
+[2023-09-11 22:06:26,383][88175] Fps is (10 sec: 11468.9, 60 sec: 14609.1, 300 sec: 12774.0). Total num frames: 37994496. Throughput: 0: 3767.1. Samples: 8491680. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:06:26,385][88175] Avg episode reward: [(0, '12.360')]
+[2023-09-11 22:06:27,744][83013] Updated weights for policy 0, policy_version 9280 (0.0009)
+[2023-09-11 22:06:31,364][83013] Updated weights for policy 0, policy_version 9290 (0.0008)
+[2023-09-11 22:06:31,383][88175] Fps is (10 sec: 11469.0, 60 sec: 14609.1, 300 sec: 12774.0). Total num frames: 38051840. Throughput: 0: 3596.5. Samples: 8508892. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 22:06:31,385][88175] Avg episode reward: [(0, '12.320')]
+[2023-09-11 22:06:34,938][83013] Updated weights for policy 0, policy_version 9300 (0.0009)
+[2023-09-11 22:06:36,383][88175] Fps is (10 sec: 11059.2, 60 sec: 14404.3, 300 sec: 12760.1). Total num frames: 38105088. Throughput: 0: 3505.0. Samples: 8517268. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:06:36,385][88175] Avg episode reward: [(0, '12.360')]
+[2023-09-11 22:06:38,531][83013] Updated weights for policy 0, policy_version 9310 (0.0008)
+[2023-09-11 22:06:41,383][88175] Fps is (10 sec: 11469.0, 60 sec: 14063.0, 300 sec: 12774.0). Total num frames: 38166528. Throughput: 0: 3342.9. Samples: 8534628. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:06:41,384][88175] Avg episode reward: [(0, '12.240')]
+[2023-09-11 22:06:42,082][83013] Updated weights for policy 0, policy_version 9320 (0.0009)
+[2023-09-11 22:06:45,549][83013] Updated weights for policy 0, policy_version 9330 (0.0008)
+[2023-09-11 22:06:46,384][88175] Fps is (10 sec: 11878.2, 60 sec: 13653.3, 300 sec: 12774.0). Total num frames: 38223872. Throughput: 0: 3176.3. Samples: 8552046. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:06:46,385][88175] Avg episode reward: [(0, '12.180')]
+[2023-09-11 22:06:49,204][83013] Updated weights for policy 0, policy_version 9340 (0.0008)
+[2023-09-11 22:06:51,383][88175] Fps is (10 sec: 11059.0, 60 sec: 13107.2, 300 sec: 12760.1). Total num frames: 38277120. Throughput: 0: 3081.6. Samples: 8560598. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:06:51,386][88175] Avg episode reward: [(0, '12.180')]
+[2023-09-11 22:06:52,728][83013] Updated weights for policy 0, policy_version 9350 (0.0008)
+[2023-09-11 22:06:56,273][83013] Updated weights for policy 0, policy_version 9360 (0.0008)
+[2023-09-11 22:06:56,383][88175] Fps is (10 sec: 11469.1, 60 sec: 12765.9, 300 sec: 12774.0). Total num frames: 38338560. Throughput: 0: 2912.9. Samples: 8577796. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
+[2023-09-11 22:06:56,385][88175] Avg episode reward: [(0, '11.930')]
+[2023-09-11 22:06:59,862][83013] Updated weights for policy 0, policy_version 9370 (0.0008)
+[2023-09-11 22:07:01,383][88175] Fps is (10 sec: 11878.4, 60 sec: 12356.2, 300 sec: 12774.0). Total num frames: 38395904. Throughput: 0: 2870.9. Samples: 8594978. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:01,384][88175] Avg episode reward: [(0, '12.010')]
+[2023-09-11 22:07:03,533][83013] Updated weights for policy 0, policy_version 9380 (0.0010)
+[2023-09-11 22:07:06,384][88175] Fps is (10 sec: 11468.6, 60 sec: 11946.7, 300 sec: 12774.0). Total num frames: 38453248. Throughput: 0: 2866.7. Samples: 8603304. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:06,385][88175] Avg episode reward: [(0, '12.170')]
+[2023-09-11 22:07:07,126][83013] Updated weights for policy 0, policy_version 9390 (0.0008)
+[2023-09-11 22:07:10,679][83013] Updated weights for policy 0, policy_version 9400 (0.0008)
+[2023-09-11 22:07:11,383][88175] Fps is (10 sec: 11059.2, 60 sec: 11468.8, 300 sec: 12690.7). Total num frames: 38506496. Throughput: 0: 2863.9. Samples: 8620556. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:11,384][88175] Avg episode reward: [(0, '12.270')]
+[2023-09-11 22:07:14,335][83013] Updated weights for policy 0, policy_version 9410 (0.0008)
+[2023-09-11 22:07:16,384][88175] Fps is (10 sec: 11059.1, 60 sec: 11400.5, 300 sec: 12607.3). Total num frames: 38563840. Throughput: 0: 2862.7. Samples: 8637716. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:16,385][88175] Avg episode reward: [(0, '12.260')]
+[2023-09-11 22:07:17,866][83013] Updated weights for policy 0, policy_version 9420 (0.0008)
+[2023-09-11 22:07:21,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11400.6, 300 sec: 12510.2). Total num frames: 38621184. Throughput: 0: 2868.6. Samples: 8646356. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:21,385][88175] Avg episode reward: [(0, '12.290')]
+[2023-09-11 22:07:21,394][83013] Updated weights for policy 0, policy_version 9430 (0.0008)
+[2023-09-11 22:07:24,901][83013] Updated weights for policy 0, policy_version 9440 (0.0008)
+[2023-09-11 22:07:26,384][88175] Fps is (10 sec: 11878.5, 60 sec: 11468.8, 300 sec: 12440.7). Total num frames: 38682624. Throughput: 0: 2869.1. Samples: 8663738. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
+[2023-09-11 22:07:26,385][88175] Avg episode reward: [(0, '12.340')]
+[2023-09-11 22:07:28,414][83013] Updated weights for policy 0, policy_version 9450 (0.0008)
+[2023-09-11 22:07:31,383][88175] Fps is (10 sec: 11878.4, 60 sec: 11468.8, 300 sec: 12357.4). Total num frames: 38739968. Throughput: 0: 2868.1. Samples: 8681110. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:31,384][88175] Avg episode reward: [(0, '12.020')]
+[2023-09-11 22:07:32,036][83013] Updated weights for policy 0, policy_version 9460 (0.0008)
+[2023-09-11 22:07:35,619][83013] Updated weights for policy 0, policy_version 9470 (0.0010)
+[2023-09-11 22:07:36,384][88175] Fps is (10 sec: 11468.7, 60 sec: 11537.0, 300 sec: 12274.1). Total num frames: 38797312. Throughput: 0: 2867.3. Samples: 8689626. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:36,386][88175] Avg episode reward: [(0, '12.300')]
+[2023-09-11 22:07:39,142][83013] Updated weights for policy 0, policy_version 9480 (0.0009)
+[2023-09-11 22:07:41,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.8, 300 sec: 12176.9). Total num frames: 38854656. Throughput: 0: 2870.6. Samples: 8706972. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:41,384][88175] Avg episode reward: [(0, '12.040')]
+[2023-09-11 22:07:42,724][83013] Updated weights for policy 0, policy_version 9490 (0.0008)
+[2023-09-11 22:07:46,352][83013] Updated weights for policy 0, policy_version 9500 (0.0008)
+[2023-09-11 22:07:46,384][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12149.1). Total num frames: 38912000. Throughput: 0: 2868.2. Samples: 8724046. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:46,385][88175] Avg episode reward: [(0, '12.230')]
+[2023-09-11 22:07:49,844][83013] Updated weights for policy 0, policy_version 9510 (0.0008)
+[2023-09-11 22:07:51,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11537.1, 300 sec: 12135.3). Total num frames: 38969344. Throughput: 0: 2874.3. Samples: 8732646. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:07:51,385][88175] Avg episode reward: [(0, '12.200')]
+[2023-09-11 22:07:53,457][83013] Updated weights for policy 0, policy_version 9520 (0.0009)
+[2023-09-11 22:07:56,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 39026688. Throughput: 0: 2870.8. Samples: 8749740. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
+[2023-09-11 22:07:56,385][88175] Avg episode reward: [(0, '12.180')]
+[2023-09-11 22:07:57,040][83013] Updated weights for policy 0, policy_version 9530 (0.0009)
+[2023-09-11 22:08:00,609][83013] Updated weights for policy 0, policy_version 9540 (0.0009)
+[2023-09-11 22:08:01,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 39084032. Throughput: 0: 2874.0. Samples: 8767046. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:08:01,384][88175] Avg episode reward: [(0, '12.220')]
+[2023-09-11 22:08:04,260][83013] Updated weights for policy 0, policy_version 9550 (0.0008)
+[2023-09-11 22:08:06,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 39141376. Throughput: 0: 2866.9. Samples: 8775366. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:08:06,385][88175] Avg episode reward: [(0, '12.120')]
+[2023-09-11 22:08:06,393][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009556_39141376.pth...
+[2023-09-11 22:08:06,443][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000008837_36196352.pth
+[2023-09-11 22:08:07,702][83013] Updated weights for policy 0, policy_version 9560 (0.0009)
+[2023-09-11 22:08:11,296][83013] Updated weights for policy 0, policy_version 9570 (0.0008)
+[2023-09-11 22:08:11,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11537.1, 300 sec: 12135.3). Total num frames: 39198720. Throughput: 0: 2868.4. Samples: 8792814. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:08:11,384][88175] Avg episode reward: [(0, '12.290')]
+[2023-09-11 22:08:14,805][83013] Updated weights for policy 0, policy_version 9580 (0.0008)
+[2023-09-11 22:08:16,383][88175] Fps is (10 sec: 11469.2, 60 sec: 11537.1, 300 sec: 12135.3). Total num frames: 39256064. Throughput: 0: 2865.9. Samples: 8810074. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:08:16,385][88175] Avg episode reward: [(0, '12.390')]
+[2023-09-11 22:08:18,327][83013] Updated weights for policy 0, policy_version 9590 (0.0008)
+[2023-09-11 22:08:21,384][88175] Fps is (10 sec: 11468.5, 60 sec: 11537.0, 300 sec: 12135.3). Total num frames: 39313408. Throughput: 0: 2871.7. Samples: 8818852. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 22:08:21,385][88175] Avg episode reward: [(0, '12.160')]
+[2023-09-11 22:08:21,947][83013] Updated weights for policy 0, policy_version 9600 (0.0008)
+[2023-09-11 22:08:25,506][83013] Updated weights for policy 0, policy_version 9610 (0.0008)
+[2023-09-11 22:08:26,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 39370752. Throughput: 0: 2869.1. Samples: 8836082. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:08:26,384][88175] Avg episode reward: [(0, '12.110')]
+[2023-09-11 22:08:29,096][83013] Updated weights for policy 0, policy_version 9620 (0.0009)
+[2023-09-11 22:08:31,383][88175] Fps is (10 sec: 11469.0, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 39428096. Throughput: 0: 2871.7. Samples: 8853270. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:08:31,385][88175] Avg episode reward: [(0, '12.380')]
+[2023-09-11 22:08:32,645][83013] Updated weights for policy 0, policy_version 9630 (0.0008)
+[2023-09-11 22:08:36,321][83013] Updated weights for policy 0, policy_version 9640 (0.0009)
+[2023-09-11 22:08:36,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 39485440. Throughput: 0: 2870.0. Samples: 8861794. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:08:36,385][88175] Avg episode reward: [(0, '12.360')]
+[2023-09-11 22:08:39,908][83013] Updated weights for policy 0, policy_version 9650 (0.0008)
+[2023-09-11 22:08:41,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 39542784. Throughput: 0: 2866.4. Samples: 8878728. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:08:41,385][88175] Avg episode reward: [(0, '12.500')]
+[2023-09-11 22:08:43,495][83013] Updated weights for policy 0, policy_version 9660 (0.0008)
+[2023-09-11 22:08:46,383][88175] Fps is (10 sec: 11468.9, 60 sec: 11468.9, 300 sec: 12121.4). Total num frames: 39600128. Throughput: 0: 2865.6. Samples: 8896000. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:08:46,385][88175] Avg episode reward: [(0, '12.200')]
+[2023-09-11 22:08:47,023][83013] Updated weights for policy 0, policy_version 9670 (0.0009)
+[2023-09-11 22:08:50,610][83013] Updated weights for policy 0, policy_version 9680 (0.0009)
+[2023-09-11 22:08:51,383][88175] Fps is (10 sec: 11468.8, 60 sec: 11468.8, 300 sec: 12121.4). Total num frames: 39657472. Throughput: 0: 2873.0. Samples: 8904648. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
+[2023-09-11 22:08:51,385][88175] Avg episode reward: [(0, '12.200')]
+[2023-09-11 22:08:54,197][83013] Updated weights for policy 0, policy_version 9690 (0.0008)
+[2023-09-11 22:08:56,383][88175] Fps is (10 sec: 11468.7, 60 sec: 11468.8, 300 sec: 12135.3). Total num frames: 39714816. Throughput: 0: 2866.2. Samples: 8921792. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:08:56,385][88175] Avg episode reward: [(0, '12.310')]
+[2023-09-11 22:08:57,536][83013] Updated weights for policy 0, policy_version 9700 (0.0009)
+[2023-09-11 22:09:00,020][83013] Updated weights for policy 0, policy_version 9710 (0.0008)
+[2023-09-11 22:09:01,383][88175] Fps is (10 sec: 13516.8, 60 sec: 11810.1, 300 sec: 12190.8). Total num frames: 39792640. Throughput: 0: 2974.7. Samples: 8943936. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
+[2023-09-11 22:09:01,385][88175] Avg episode reward: [(0, '12.140')]
+[2023-09-11 22:09:02,448][83013] Updated weights for policy 0, policy_version 9720 (0.0009)
+[2023-09-11 22:09:04,903][83013] Updated weights for policy 0, policy_version 9730 (0.0008)
+[2023-09-11 22:09:06,383][88175] Fps is (10 sec: 15974.3, 60 sec: 12219.8, 300 sec: 12274.1). Total num frames: 39874560. Throughput: 0: 3056.9. Samples: 8956414. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 22:09:06,385][88175] Avg episode reward: [(0, '12.280')]
+[2023-09-11 22:09:07,402][83013] Updated weights for policy 0, policy_version 9740 (0.0008)
+[2023-09-11 22:09:09,886][83013] Updated weights for policy 0, policy_version 9750 (0.0008)
+[2023-09-11 22:09:11,383][88175] Fps is (10 sec: 16793.6, 60 sec: 12697.6, 300 sec: 12371.3). Total num frames: 39960576. Throughput: 0: 3225.4. Samples: 8981226. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
+[2023-09-11 22:09:11,385][88175] Avg episode reward: [(0, '12.270')]
+[2023-09-11 22:09:12,337][83013] Updated weights for policy 0, policy_version 9760 (0.0008)
+[2023-09-11 22:09:14,066][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-11 22:09:14,066][88175] Component Batcher_0 stopped!
+[2023-09-11 22:09:14,066][82980] Stopping Batcher_0...
+[2023-09-11 22:09:14,079][82980] Loop batcher_evt_loop terminating...
+[2023-09-11 22:09:14,083][83014] Stopping RolloutWorker_w1...
+[2023-09-11 22:09:14,083][83014] Loop rollout_proc1_evt_loop terminating...
+[2023-09-11 22:09:14,083][83013] Weights refcount: 2 0
+[2023-09-11 22:09:14,083][88175] Component RolloutWorker_w1 stopped!
+[2023-09-11 22:09:14,084][83068] Stopping RolloutWorker_w5...
+[2023-09-11 22:09:14,084][83013] Stopping InferenceWorker_p0-w0...
+[2023-09-11 22:09:14,085][83068] Loop rollout_proc5_evt_loop terminating...
+[2023-09-11 22:09:14,085][83013] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-11 22:09:14,084][88175] Component RolloutWorker_w5 stopped!
+[2023-09-11 22:09:14,085][88175] Component InferenceWorker_p0-w0 stopped!
+[2023-09-11 22:09:14,086][83065] Stopping RolloutWorker_w4...
+[2023-09-11 22:09:14,086][83065] Loop rollout_proc4_evt_loop terminating...
+[2023-09-11 22:09:14,086][88175] Component RolloutWorker_w4 stopped!
+[2023-09-11 22:09:14,087][83015] Stopping RolloutWorker_w2...
+[2023-09-11 22:09:14,087][83015] Loop rollout_proc2_evt_loop terminating...
+[2023-09-11 22:09:14,087][83016] Stopping RolloutWorker_w0...
+[2023-09-11 22:09:14,087][88175] Component RolloutWorker_w2 stopped!
+[2023-09-11 22:09:14,088][83016] Loop rollout_proc0_evt_loop terminating...
+[2023-09-11 22:09:14,088][83069] Stopping RolloutWorker_w6...
+[2023-09-11 22:09:14,088][83069] Loop rollout_proc6_evt_loop terminating...
+[2023-09-11 22:09:14,088][88175] Component RolloutWorker_w0 stopped!
+[2023-09-11 22:09:14,089][83070] Stopping RolloutWorker_w7...
+[2023-09-11 22:09:14,090][83070] Loop rollout_proc7_evt_loop terminating...
+[2023-09-11 22:09:14,090][83067] Stopping RolloutWorker_w3...
+[2023-09-11 22:09:14,089][88175] Component RolloutWorker_w6 stopped!
+[2023-09-11 22:09:14,090][83067] Loop rollout_proc3_evt_loop terminating...
+[2023-09-11 22:09:14,090][88175] Component RolloutWorker_w7 stopped!
+[2023-09-11 22:09:14,091][88175] Component RolloutWorker_w3 stopped!
+[2023-09-11 22:09:14,124][82980] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009214_37740544.pth
+[2023-09-11 22:09:14,131][82980] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-11 22:09:14,191][82980] Stopping LearnerWorker_p0...
+[2023-09-11 22:09:14,191][82980] Loop learner_proc0_evt_loop terminating...
+[2023-09-11 22:09:14,191][88175] Component LearnerWorker_p0 stopped!
+[2023-09-11 22:09:14,193][88175] Waiting for process learner_proc0 to stop...
+[2023-09-11 22:09:14,935][88175] Waiting for process inference_proc0-0 to join...
+[2023-09-11 22:09:14,937][88175] Waiting for process rollout_proc0 to join...
+[2023-09-11 22:09:14,939][88175] Waiting for process rollout_proc1 to join...
+[2023-09-11 22:09:14,941][88175] Waiting for process rollout_proc2 to join...
+[2023-09-11 22:09:14,942][88175] Waiting for process rollout_proc3 to join...
+[2023-09-11 22:09:14,944][88175] Waiting for process rollout_proc4 to join...
+[2023-09-11 22:09:14,945][88175] Waiting for process rollout_proc5 to join...
+[2023-09-11 22:09:14,947][88175] Waiting for process rollout_proc6 to join...
+[2023-09-11 22:09:14,948][88175] Waiting for process rollout_proc7 to join...
+[2023-09-11 22:09:14,950][88175] Batcher 0 profile tree view:
+batching: 146.3245, releasing_batches: 0.2692
+[2023-09-11 22:09:14,951][88175] InferenceWorker_p0-w0 profile tree view:
+wait_policy: 0.0000
+  wait_policy_total: 40.6287
+update_model: 25.0029
+  weight_update: 0.0008
+one_step: 0.0023
+  handle_policy_step: 2772.6195
+    deserialize: 69.0043, stack: 9.1246, obs_to_device_normalize: 504.1583, forward: 1700.2175, send_messages: 101.5437
+    prepare_outputs: 317.1934
+      to_cpu: 235.2847
+[2023-09-11 22:09:14,952][88175] Learner 0 profile tree view:
+misc: 0.0505, prepare_batch: 122.1029
+train: 601.8704
+  epoch_init: 0.0465, minibatch_init: 0.0483, losses_postprocess: 6.4577, kl_divergence: 5.0091, after_optimizer: 9.6737
+  calculate_losses: 179.4940
+    losses_init: 0.0275, forward_head: 4.5056, bptt_initial: 137.0682, tail: 6.0814, advantages_returns: 1.0238, losses: 21.0884
+    bptt: 8.3221
+      bptt_forward_core: 7.9874
+  update: 398.1058
+    clip: 320.6357
+[2023-09-11 22:09:14,953][88175] RolloutWorker_w0 profile tree view:
+wait_for_trajectories: 1.6238, enqueue_policy_requests: 79.5078, env_step: 1294.3296, overhead: 62.3789, complete_rollouts: 2.4966
+save_policy_outputs: 103.2407
+  split_output_tensors: 36.2783
+[2023-09-11 22:09:14,954][88175] RolloutWorker_w7 profile tree view:
+wait_for_trajectories: 1.5912, enqueue_policy_requests: 79.7679, env_step: 1292.9108, overhead: 63.2606, complete_rollouts: 2.5170
+save_policy_outputs: 103.2870
+  split_output_tensors: 36.3240
+[2023-09-11 22:09:14,955][88175] Loop Runner_EvtLoop terminating...
+[2023-09-11 22:09:14,956][88175] Runner profile tree view:
+main_loop: 2942.8870
+[2023-09-11 22:09:14,957][88175] Collected {0: 40005632}, FPS: 12230.0
+[2023-09-11 22:09:14,984][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-11 22:09:14,985][88175] Overriding arg 'num_workers' with value 1 passed from command line
+[2023-09-11 22:09:14,986][88175] Adding new argument 'no_render'=True that is not in the saved config file!
+[2023-09-11 22:09:14,987][88175] Adding new argument 'save_video'=True that is not in the saved config file!
+[2023-09-11 22:09:14,989][88175] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
+[2023-09-11 22:09:14,990][88175] Adding new argument 'video_name'=None that is not in the saved config file!
+[2023-09-11 22:09:14,991][88175] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
+[2023-09-11 22:09:14,992][88175] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
+[2023-09-11 22:09:14,993][88175] Adding new argument 'push_to_hub'=True that is not in the saved config file!
+[2023-09-11 22:09:14,993][88175] Adding new argument 'hf_repository'='MattStammers/_vizdoom_defend_the_center' that is not in the saved config file!
+[2023-09-11 22:09:14,994][88175] Adding new argument 'policy_index'=0 that is not in the saved config file!
+[2023-09-11 22:09:14,996][88175] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
+[2023-09-11 22:09:14,997][88175] Adding new argument 'train_script'=None that is not in the saved config file!
+[2023-09-11 22:09:14,998][88175] Adding new argument 'enjoy_script'=None that is not in the saved config file!
+[2023-09-11 22:09:14,999][88175] Using frameskip 1 and render_action_repeat=4 for evaluation
+[2023-09-11 22:09:15,031][88175] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-11 22:09:15,033][88175] RunningMeanStd input shape: (1,)
+[2023-09-11 22:09:15,044][88175] ConvEncoder: input_channels=3
+[2023-09-11 22:09:15,081][88175] Conv encoder output size: 512
+[2023-09-11 22:09:15,081][88175] Policy head output size: 512
+[2023-09-11 22:09:15,114][88175] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-11 22:09:15,640][88175] Num frames 100...
+[2023-09-11 22:09:15,808][88175] Num frames 200...
+[2023-09-11 22:09:15,951][88175] Num frames 300...
+[2023-09-11 22:09:16,097][88175] Num frames 400...
+[2023-09-11 22:09:16,248][88175] Num frames 500...
+[2023-09-11 22:09:16,399][88175] Num frames 600...
+[2023-09-11 22:09:16,599][88175] Avg episode rewards: #0: 10.000, true rewards: #0: 10.000
+[2023-09-11 22:09:16,601][88175] Avg episode reward: 10.000, avg true_objective: 10.000
+[2023-09-11 22:09:16,617][88175] Num frames 700...
+[2023-09-11 22:09:16,769][88175] Num frames 800...
+[2023-09-11 22:09:16,912][88175] Num frames 900...
+[2023-09-11 22:09:17,066][88175] Num frames 1000...
+[2023-09-11 22:09:17,213][88175] Num frames 1100...
+[2023-09-11 22:09:17,364][88175] Num frames 1200...
+[2023-09-11 22:09:17,506][88175] Num frames 1300...
+[2023-09-11 22:09:17,652][88175] Num frames 1400...
+[2023-09-11 22:09:17,718][88175] Avg episode rewards: #0: 10.000, true rewards: #0: 10.000
+[2023-09-11 22:09:17,720][88175] Avg episode reward: 10.000, avg true_objective: 10.000
+[2023-09-11 22:09:17,870][88175] Num frames 1500...
+[2023-09-11 22:09:18,017][88175] Num frames 1600...
+[2023-09-11 22:09:18,167][88175] Num frames 1700...
+[2023-09-11 22:09:18,320][88175] Num frames 1800...
+[2023-09-11 22:09:18,461][88175] Num frames 1900...
+[2023-09-11 22:09:18,600][88175] Num frames 2000...
+[2023-09-11 22:09:18,744][88175] Num frames 2100...
+[2023-09-11 22:09:18,903][88175] Avg episode rewards: #0: 10.667, true rewards: #0: 10.667
+[2023-09-11 22:09:18,905][88175] Avg episode reward: 10.667, avg true_objective: 10.667
+[2023-09-11 22:09:18,957][88175] Num frames 2200...
+[2023-09-11 22:09:19,096][88175] Num frames 2300...
+[2023-09-11 22:09:19,241][88175] Num frames 2400...
+[2023-09-11 22:09:19,390][88175] Num frames 2500...
+[2023-09-11 22:09:19,605][88175] Num frames 2600...
+[2023-09-11 22:09:19,782][88175] Num frames 2700...
+[2023-09-11 22:09:19,964][88175] Num frames 2800...
+[2023-09-11 22:09:20,149][88175] Avg episode rewards: #0: 10.500, true rewards: #0: 10.500
+[2023-09-11 22:09:20,151][88175] Avg episode reward: 10.500, avg true_objective: 10.500
+[2023-09-11 22:09:20,186][88175] Num frames 2900...
+[2023-09-11 22:09:20,339][88175] Num frames 3000...
+[2023-09-11 22:09:20,481][88175] Num frames 3100...
+[2023-09-11 22:09:20,629][88175] Num frames 3200...
+[2023-09-11 22:09:20,779][88175] Num frames 3300...
+[2023-09-11 22:09:20,942][88175] Num frames 3400...
+[2023-09-11 22:09:21,094][88175] Num frames 3500...
+[2023-09-11 22:09:21,258][88175] Num frames 3600...
+[2023-09-11 22:09:21,460][88175] Avg episode rewards: #0: 11.000, true rewards: #0: 11.000
+[2023-09-11 22:09:21,462][88175] Avg episode reward: 11.000, avg true_objective: 11.000
+[2023-09-11 22:09:21,473][88175] Num frames 3700...
+[2023-09-11 22:09:21,620][88175] Num frames 3800...
+[2023-09-11 22:09:21,786][88175] Num frames 3900...
+[2023-09-11 22:09:21,931][88175] Num frames 4000...
+[2023-09-11 22:09:22,077][88175] Num frames 4100...
+[2023-09-11 22:09:22,254][88175] Num frames 4200...
+[2023-09-11 22:09:22,402][88175] Num frames 4300...
+[2023-09-11 22:09:22,549][88175] Num frames 4400...
+[2023-09-11 22:09:22,703][88175] Num frames 4500...
+[2023-09-11 22:09:22,853][88175] Num frames 4600...
+[2023-09-11 22:09:22,941][88175] Avg episode rewards: #0: 10.833, true rewards: #0: 10.833
+[2023-09-11 22:09:22,942][88175] Avg episode reward: 10.833, avg true_objective: 10.833
+[2023-09-11 22:09:23,062][88175] Num frames 4700...
+[2023-09-11 22:09:23,214][88175] Num frames 4800...
+[2023-09-11 22:09:23,382][88175] Num frames 4900...
+[2023-09-11 22:09:23,546][88175] Num frames 5000...
+[2023-09-11 22:09:23,705][88175] Num frames 5100...
+[2023-09-11 22:09:23,891][88175] Num frames 5200...
+[2023-09-11 22:09:24,033][88175] Num frames 5300...
+[2023-09-11 22:09:24,180][88175] Num frames 5400...
+[2023-09-11 22:09:24,319][88175] Avg episode rewards: #0: 11.000, true rewards: #0: 11.000
+[2023-09-11 22:09:24,320][88175] Avg episode reward: 11.000, avg true_objective: 11.000
+[2023-09-11 22:09:24,393][88175] Num frames 5500...
+[2023-09-11 22:09:24,544][88175] Num frames 5600...
+[2023-09-11 22:09:24,702][88175] Num frames 5700...
+[2023-09-11 22:09:24,861][88175] Num frames 5800...
+[2023-09-11 22:09:25,005][88175] Num frames 5900...
+[2023-09-11 22:09:25,152][88175] Num frames 6000...
+[2023-09-11 22:09:25,346][88175] Num frames 6100...
+[2023-09-11 22:09:25,550][88175] Avg episode rewards: #0: 10.875, true rewards: #0: 10.875
+[2023-09-11 22:09:25,552][88175] Avg episode reward: 10.875, avg true_objective: 10.875
+[2023-09-11 22:09:25,586][88175] Num frames 6200...
+[2023-09-11 22:09:25,737][88175] Num frames 6300...
+[2023-09-11 22:09:25,909][88175] Num frames 6400...
+[2023-09-11 22:09:26,075][88175] Num frames 6500...
+[2023-09-11 22:09:26,240][88175] Num frames 6600...
+[2023-09-11 22:09:26,392][88175] Num frames 6700...
+[2023-09-11 22:09:26,572][88175] Avg episode rewards: #0: 10.667, true rewards: #0: 10.667
+[2023-09-11 22:09:26,574][88175] Avg episode reward: 10.667, avg true_objective: 10.667
+[2023-09-11 22:09:26,627][88175] Num frames 6800...
+[2023-09-11 22:09:26,786][88175] Num frames 6900...
+[2023-09-11 22:09:26,963][88175] Num frames 7000...
+[2023-09-11 22:09:27,151][88175] Num frames 7100...
+[2023-09-11 22:09:27,307][88175] Num frames 7200...
+[2023-09-11 22:09:27,484][88175] Num frames 7300...
+[2023-09-11 22:09:27,656][88175] Num frames 7400...
+[2023-09-11 22:09:27,821][88175] Num frames 7500...
+[2023-09-11 22:09:27,947][88175] Avg episode rewards: #0: 10.600, true rewards: #0: 10.600
+[2023-09-11 22:09:27,948][88175] Avg episode reward: 10.600, avg true_objective: 10.600
+[2023-09-11 22:09:42,081][88175] Replay video saved to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/replay.mp4!
+[2023-09-11 22:09:46,906][88175] The model has been pushed to https://huggingface.co/MattStammers/_vizdoom_defend_the_center
+[2023-09-12 05:34:19,600][88175] Environment doom_basic already registered, overwriting...
+[2023-09-12 05:34:19,603][88175] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 05:34:19,607][88175] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 05:34:19,608][88175] Environment doom_dm already registered, overwriting...
+[2023-09-12 05:34:19,609][88175] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 05:34:19,610][88175] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 05:34:19,611][88175] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 05:34:19,611][88175] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 05:34:19,612][88175] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 05:34:19,612][88175] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 05:34:19,613][88175] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 05:34:19,614][88175] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 05:34:19,615][88175] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 05:34:19,616][88175] Environment doom_battle already registered, overwriting...
+[2023-09-12 05:34:19,618][88175] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 05:34:19,619][88175] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 05:34:19,620][88175] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 05:34:19,621][88175] Environment doom_duel already registered, overwriting...
+[2023-09-12 05:34:19,622][88175] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 05:34:19,623][88175] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 05:34:19,624][88175] register_encoder_factory: <function make_vizdoom_encoder at 0x7fdf25a3f5b0>
+[2023-09-12 05:34:19,650][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 05:34:19,651][88175] Overriding arg 'env' with value 'doom_my_way_home' passed from command line
+[2023-09-12 05:34:19,652][88175] Overriding arg 'train_for_env_steps' with value 4000000 passed from command line
+[2023-09-12 05:34:19,662][88175] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 05:34:19,663][88175] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 05:34:19,664][88175] Weights and Biases integration disabled
+[2023-09-12 05:34:19,668][88175] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 05:34:21,766][88175] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_my_way_home
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 05:34:21,768][88175] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 05:34:22,739][88175] Rollout worker 0 uses device cpu
+[2023-09-12 05:34:22,740][88175] Rollout worker 1 uses device cpu
+[2023-09-12 05:34:22,742][88175] Rollout worker 2 uses device cpu
+[2023-09-12 05:34:22,744][88175] Rollout worker 3 uses device cpu
+[2023-09-12 05:34:22,745][88175] Rollout worker 4 uses device cpu
+[2023-09-12 05:34:22,747][88175] Rollout worker 5 uses device cpu
+[2023-09-12 05:34:22,748][88175] Rollout worker 6 uses device cpu
+[2023-09-12 05:34:22,749][88175] Rollout worker 7 uses device cpu
+[2023-09-12 05:34:22,781][88175] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 05:34:22,782][88175] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 05:34:22,808][88175] Starting all processes...
+[2023-09-12 05:34:22,809][88175] Starting process learner_proc0
+[2023-09-12 05:34:24,543][88175] Starting all processes...
+[2023-09-12 05:34:24,545][00407] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 05:34:24,545][00407] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 05:34:24,549][88175] Starting process inference_proc0-0
+[2023-09-12 05:34:24,549][88175] Starting process rollout_proc0
+[2023-09-12 05:34:24,550][88175] Starting process rollout_proc1
+[2023-09-12 05:34:24,550][88175] Starting process rollout_proc2
+[2023-09-12 05:34:24,551][88175] Starting process rollout_proc3
+[2023-09-12 05:34:24,563][00407] Num visible devices: 1
+[2023-09-12 05:34:24,551][88175] Starting process rollout_proc4
+[2023-09-12 05:34:24,552][88175] Starting process rollout_proc5
+[2023-09-12 05:34:24,553][88175] Starting process rollout_proc6
+[2023-09-12 05:34:24,554][88175] Starting process rollout_proc7
+[2023-09-12 05:34:24,619][00407] Starting seed is not provided
+[2023-09-12 05:34:24,619][00407] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 05:34:24,620][00407] Initializing actor-critic model on device cuda:0
+[2023-09-12 05:34:24,620][00407] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 05:34:24,621][00407] RunningMeanStd input shape: (1,)
+[2023-09-12 05:34:24,635][00407] ConvEncoder: input_channels=3
+[2023-09-12 05:34:24,866][00407] Conv encoder output size: 512
+[2023-09-12 05:34:24,866][00407] Policy head output size: 512
+[2023-09-12 05:34:24,880][00407] Created Actor Critic model with architecture:
+[2023-09-12 05:34:24,881][00407] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(512, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=6, bias=True)
+  )
+)
+[2023-09-12 05:34:26,026][00407] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 05:34:26,026][00407] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-12 05:34:26,059][00407] Loading model from checkpoint
+[2023-09-12 05:34:26,060][00407] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([6, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([6]).
+[2023-09-12 05:34:26,062][00407] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([6, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([6]). in evt loop learner_proc0_evt_loop
+[2023-09-12 05:34:26,555][00626] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 05:34:26,577][00629] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 05:34:26,619][00627] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 05:34:26,628][00625] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 05:34:26,633][00589] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 05:34:26,633][00589] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 05:34:26,675][00589] Num visible devices: 1
+[2023-09-12 05:34:26,705][00639] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 05:34:26,763][00590] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 05:34:26,805][00637] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 05:34:26,811][00636] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 05:34:42,775][88175] Heartbeat connected on Batcher_0
+[2023-09-12 05:34:42,782][88175] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 05:34:42,787][88175] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 05:34:42,790][88175] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 05:34:42,793][88175] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 05:34:42,796][88175] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 05:34:42,799][88175] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 05:34:42,802][88175] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 05:34:42,805][88175] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 05:34:42,808][88175] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 05:44:19,669][88175] Components not started: LearnerWorker_p0, wait_time=600.0 seconds
+[2023-09-12 05:54:19,668][88175] Components not started: LearnerWorker_p0, wait_time=1200.0 seconds
+[2023-09-12 06:04:19,669][88175] Components not started: LearnerWorker_p0, wait_time=1800.0 seconds
+[2023-09-12 06:04:19,671][88175] Components take too long to start: LearnerWorker_p0. Aborting the experiment!
+
+
+
+[2023-09-12 06:04:19,674][00637] Stopping RolloutWorker_w6...
+[2023-09-12 06:04:19,674][00625] Stopping RolloutWorker_w2...
+[2023-09-12 06:04:19,674][00629] Stopping RolloutWorker_w5...
+[2023-09-12 06:04:19,674][00590] Stopping RolloutWorker_w3...
+[2023-09-12 06:04:19,674][00589] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:04:19,674][00639] Stopping RolloutWorker_w7...
+[2023-09-12 06:04:19,674][00627] Stopping RolloutWorker_w0...
+[2023-09-12 06:04:19,674][00636] Stopping RolloutWorker_w4...
+[2023-09-12 06:04:19,674][00407] Stopping Batcher_0...
+[2023-09-12 06:04:19,674][00626] Stopping RolloutWorker_w1...
+[2023-09-12 06:04:19,674][00637] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:04:19,674][00625] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:04:19,674][00629] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:04:19,675][00590] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:04:19,675][00589] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:04:19,675][00639] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:04:19,675][00407] Loop batcher_evt_loop terminating...
+[2023-09-12 06:04:19,675][00627] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:04:19,675][00636] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:04:19,675][00626] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:04:19,675][88175] Component RolloutWorker_w6 stopped!
+[2023-09-12 06:04:19,677][88175] Waiting for ['Batcher_0', 'LearnerWorker_p0', 'InferenceWorker_p0-w0', 'RolloutWorker_w0', 'RolloutWorker_w1', 'RolloutWorker_w2', 'RolloutWorker_w3', 'RolloutWorker_w4', 'RolloutWorker_w5', 'RolloutWorker_w7'] to stop...
+[2023-09-12 06:04:19,679][88175] Component RolloutWorker_w2 stopped!
+[2023-09-12 06:04:19,680][88175] Waiting for ['Batcher_0', 'LearnerWorker_p0', 'InferenceWorker_p0-w0', 'RolloutWorker_w0', 'RolloutWorker_w1', 'RolloutWorker_w3', 'RolloutWorker_w4', 'RolloutWorker_w5', 'RolloutWorker_w7'] to stop...
+[2023-09-12 06:04:19,682][88175] Component RolloutWorker_w5 stopped!
+[2023-09-12 06:04:19,683][88175] Waiting for ['Batcher_0', 'LearnerWorker_p0', 'InferenceWorker_p0-w0', 'RolloutWorker_w0', 'RolloutWorker_w1', 'RolloutWorker_w3', 'RolloutWorker_w4', 'RolloutWorker_w7'] to stop...
+[2023-09-12 06:04:19,685][88175] Component RolloutWorker_w4 stopped!
+[2023-09-12 06:04:19,687][88175] Waiting for ['Batcher_0', 'LearnerWorker_p0', 'InferenceWorker_p0-w0', 'RolloutWorker_w0', 'RolloutWorker_w1', 'RolloutWorker_w3', 'RolloutWorker_w7'] to stop...
+[2023-09-12 06:04:19,688][88175] Component RolloutWorker_w3 stopped!
+[2023-09-12 06:04:19,689][88175] Waiting for ['Batcher_0', 'LearnerWorker_p0', 'InferenceWorker_p0-w0', 'RolloutWorker_w0', 'RolloutWorker_w1', 'RolloutWorker_w7'] to stop...
+[2023-09-12 06:04:19,690][88175] Component RolloutWorker_w0 stopped!
+[2023-09-12 06:04:19,691][88175] Waiting for ['Batcher_0', 'LearnerWorker_p0', 'InferenceWorker_p0-w0', 'RolloutWorker_w1', 'RolloutWorker_w7'] to stop...
+[2023-09-12 06:04:19,692][88175] Component InferenceWorker_p0-w0 stopped!
+[2023-09-12 06:04:19,693][88175] Waiting for ['Batcher_0', 'LearnerWorker_p0', 'RolloutWorker_w1', 'RolloutWorker_w7'] to stop...
+[2023-09-12 06:04:19,694][88175] Component RolloutWorker_w7 stopped!
+[2023-09-12 06:04:19,695][88175] Waiting for ['Batcher_0', 'LearnerWorker_p0', 'RolloutWorker_w1'] to stop...
+[2023-09-12 06:04:19,696][88175] Component Batcher_0 stopped!
+[2023-09-12 06:04:19,697][88175] Waiting for ['LearnerWorker_p0', 'RolloutWorker_w1'] to stop...
+[2023-09-12 06:04:19,698][88175] Component RolloutWorker_w1 stopped!
+[2023-09-12 06:04:19,698][88175] Waiting for ['LearnerWorker_p0'] to stop...
+[2023-09-12 06:23:57,105][88175] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 88175], exiting...
+[2023-09-12 06:23:57,107][88175] Runner profile tree view:
+main_loop: 2974.2995
+[2023-09-12 06:23:57,108][88175] Collected {}, FPS: 0.0
+[2023-09-12 06:24:10,201][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:24:10,202][88175] Overriding arg 'num_workers' with value 1 passed from command line
+[2023-09-12 06:24:10,203][88175] Adding new argument 'no_render'=True that is not in the saved config file!
+[2023-09-12 06:24:10,204][88175] Adding new argument 'save_video'=True that is not in the saved config file!
+[2023-09-12 06:24:10,204][88175] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
+[2023-09-12 06:24:10,205][88175] Adding new argument 'video_name'=None that is not in the saved config file!
+[2023-09-12 06:24:10,205][88175] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
+[2023-09-12 06:24:10,206][88175] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
+[2023-09-12 06:24:10,207][88175] Adding new argument 'push_to_hub'=True that is not in the saved config file!
+[2023-09-12 06:24:10,207][88175] Adding new argument 'hf_repository'='MattStammers/vizdoom_my_way_home' that is not in the saved config file!
+[2023-09-12 06:24:10,208][88175] Adding new argument 'policy_index'=0 that is not in the saved config file!
+[2023-09-12 06:24:10,209][88175] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
+[2023-09-12 06:24:10,209][88175] Adding new argument 'train_script'=None that is not in the saved config file!
+[2023-09-12 06:24:10,210][88175] Adding new argument 'enjoy_script'=None that is not in the saved config file!
+[2023-09-12 06:24:10,211][88175] Using frameskip 1 and render_action_repeat=4 for evaluation
+[2023-09-12 06:24:10,242][88175] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:24:10,243][88175] RunningMeanStd input shape: (1,)
+[2023-09-12 06:24:10,253][88175] ConvEncoder: input_channels=3
+[2023-09-12 06:24:10,290][88175] Conv encoder output size: 512
+[2023-09-12 06:24:10,292][88175] Policy head output size: 512
+[2023-09-12 06:24:10,332][88175] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-12 06:24:41,514][88175] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:24:41,517][88175] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:24:41,519][88175] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:24:41,520][88175] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:24:41,522][88175] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:24:41,523][88175] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:24:41,524][88175] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:24:41,526][88175] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:24:41,527][88175] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:24:41,528][88175] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:24:41,529][88175] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:24:41,529][88175] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:24:41,531][88175] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:24:41,532][88175] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:24:41,532][88175] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:24:41,533][88175] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:24:41,534][88175] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:24:41,535][88175] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:24:41,536][88175] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:24:41,537][88175] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:24:41,538][88175] register_encoder_factory: <function make_vizdoom_encoder at 0x7fdf25a3f5b0>
+[2023-09-12 06:24:41,566][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:24:41,571][88175] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:24:41,572][88175] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:24:41,573][88175] Weights and Biases integration disabled
+[2023-09-12 06:24:41,575][88175] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:24:43,589][88175] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_my_way_home
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:24:43,592][88175] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:24:44,564][88175] Rollout worker 0 uses device cpu
+[2023-09-12 06:24:44,566][88175] Rollout worker 1 uses device cpu
+[2023-09-12 06:24:44,568][88175] Rollout worker 2 uses device cpu
+[2023-09-12 06:24:44,569][88175] Rollout worker 3 uses device cpu
+[2023-09-12 06:24:44,572][88175] Rollout worker 4 uses device cpu
+[2023-09-12 06:24:44,573][88175] Rollout worker 5 uses device cpu
+[2023-09-12 06:24:44,574][88175] Rollout worker 6 uses device cpu
+[2023-09-12 06:24:44,576][88175] Rollout worker 7 uses device cpu
+[2023-09-12 06:24:44,605][88175] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:24:44,606][88175] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:24:44,633][88175] Starting all processes...
+[2023-09-12 06:24:44,634][88175] Starting process learner_proc0
+[2023-09-12 06:24:46,330][88175] Starting all processes...
+[2023-09-12 06:24:46,332][28343] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:24:46,332][28343] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:24:46,336][88175] Starting process inference_proc0-0
+[2023-09-12 06:24:46,337][88175] Starting process rollout_proc0
+[2023-09-12 06:24:46,338][88175] Starting process rollout_proc1
+[2023-09-12 06:24:46,338][88175] Starting process rollout_proc2
+[2023-09-12 06:24:46,339][88175] Starting process rollout_proc3
+[2023-09-12 06:24:46,339][88175] Starting process rollout_proc4
+[2023-09-12 06:24:46,340][88175] Starting process rollout_proc5
+[2023-09-12 06:24:46,373][28343] Num visible devices: 1
+[2023-09-12 06:24:46,340][88175] Starting process rollout_proc6
+[2023-09-12 06:24:46,341][88175] Starting process rollout_proc7
+[2023-09-12 06:24:46,412][28343] Starting seed is not provided
+[2023-09-12 06:24:46,412][28343] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:24:46,413][28343] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:24:46,413][28343] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:24:46,414][28343] RunningMeanStd input shape: (1,)
+[2023-09-12 06:24:46,426][28343] ConvEncoder: input_channels=3
+[2023-09-12 06:24:46,629][28343] Conv encoder output size: 512
+[2023-09-12 06:24:46,629][28343] Policy head output size: 512
+[2023-09-12 06:24:46,653][28343] Created Actor Critic model with architecture:
+[2023-09-12 06:24:46,654][28343] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(512, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=6, bias=True)
+  )
+)
+[2023-09-12 06:24:47,777][28343] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:24:47,777][28343] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-12 06:24:47,813][28343] Loading model from checkpoint
+[2023-09-12 06:24:47,814][28343] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([6, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([6]).
+[2023-09-12 06:24:47,815][28343] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([6, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([6]). in evt loop learner_proc0_evt_loop
+[2023-09-12 06:24:48,244][28446] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:24:48,247][28447] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:24:48,307][28445] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:24:48,307][28445] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:24:48,321][28451] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:24:48,326][28445] Num visible devices: 1
+[2023-09-12 06:24:48,361][28450] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:24:48,374][28486] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:24:48,385][28484] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:24:48,456][28444] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:24:48,638][28448] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:25:04,599][88175] Heartbeat connected on Batcher_0
+[2023-09-12 06:25:04,606][88175] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 06:25:04,611][88175] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 06:25:04,614][88175] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 06:25:04,617][88175] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 06:25:04,620][88175] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 06:25:04,624][88175] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 06:25:04,627][88175] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 06:25:04,630][88175] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 06:25:04,632][88175] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 06:25:23,833][88175] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 88175], exiting...
+[2023-09-12 06:25:23,835][28448] Stopping RolloutWorker_w3...
+[2023-09-12 06:25:23,835][28486] Stopping RolloutWorker_w6...
+[2023-09-12 06:25:23,835][28484] Stopping RolloutWorker_w7...
+[2023-09-12 06:25:23,835][28448] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:25:23,835][28447] Stopping RolloutWorker_w2...
+[2023-09-12 06:25:23,835][28444] Stopping RolloutWorker_w0...
+[2023-09-12 06:25:23,835][28451] Stopping RolloutWorker_w5...
+[2023-09-12 06:25:23,835][28446] Stopping RolloutWorker_w1...
+[2023-09-12 06:25:23,835][28343] Stopping Batcher_0...
+[2023-09-12 06:25:23,835][28450] Stopping RolloutWorker_w4...
+[2023-09-12 06:25:23,835][28445] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:25:23,836][28486] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:25:23,836][28447] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:25:23,836][28444] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:25:23,836][28343] Loop batcher_evt_loop terminating...
+[2023-09-12 06:25:23,836][28484] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:25:23,836][28446] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:25:23,836][28451] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:25:23,836][28445] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:25:23,836][28450] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:25:23,835][88175] Runner profile tree view:
+main_loop: 39.2025
+[2023-09-12 06:25:23,838][88175] Collected {}, FPS: 0.0
+[2023-09-12 06:25:25,283][88175] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:25:25,285][88175] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:25:25,286][88175] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:25:25,287][88175] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:25:25,288][88175] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:25:25,289][88175] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:25:25,290][88175] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:25:25,290][88175] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:25:25,292][88175] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:25:25,293][88175] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:25:25,293][88175] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:25:25,294][88175] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:25:25,294][88175] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:25:25,295][88175] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:25:25,296][88175] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:25:25,296][88175] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:25:25,297][88175] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:25:25,297][88175] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:25:25,298][88175] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:25:25,300][88175] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:25:25,300][88175] register_encoder_factory: <function make_vizdoom_encoder at 0x7fdf25a3f5b0>
+[2023-09-12 06:25:25,322][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:25:25,323][88175] Overriding arg 'env' with value 'doom_dm' passed from command line
+[2023-09-12 06:25:25,327][88175] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:25:25,328][88175] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:25:25,328][88175] Weights and Biases integration disabled
+[2023-09-12 06:25:25,330][88175] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:25:27,462][88175] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_dm
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:25:27,464][88175] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:25:28,437][88175] Rollout worker 0 uses device cpu
+[2023-09-12 06:25:28,438][88175] Rollout worker 1 uses device cpu
+[2023-09-12 06:25:28,440][88175] Rollout worker 2 uses device cpu
+[2023-09-12 06:25:28,441][88175] Rollout worker 3 uses device cpu
+[2023-09-12 06:25:28,442][88175] Rollout worker 4 uses device cpu
+[2023-09-12 06:25:28,443][88175] Rollout worker 5 uses device cpu
+[2023-09-12 06:25:28,446][88175] Rollout worker 6 uses device cpu
+[2023-09-12 06:25:28,447][88175] Rollout worker 7 uses device cpu
+[2023-09-12 06:25:28,627][88175] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:25:28,628][88175] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:25:28,655][88175] Starting all processes...
+[2023-09-12 06:25:28,656][88175] Starting process learner_proc0
+[2023-09-12 06:25:30,243][88175] Starting all processes...
+[2023-09-12 06:25:30,245][31355] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:25:30,245][31355] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:25:30,249][88175] Starting process inference_proc0-0
+[2023-09-12 06:25:30,249][88175] Starting process rollout_proc0
+[2023-09-12 06:25:30,250][88175] Starting process rollout_proc1
+[2023-09-12 06:25:30,250][88175] Starting process rollout_proc2
+[2023-09-12 06:25:30,251][88175] Starting process rollout_proc3
+[2023-09-12 06:25:30,282][31355] Num visible devices: 1
+[2023-09-12 06:25:30,252][88175] Starting process rollout_proc4
+[2023-09-12 06:25:30,253][88175] Starting process rollout_proc5
+[2023-09-12 06:25:30,264][88175] Starting process rollout_proc6
+[2023-09-12 06:25:30,264][88175] Starting process rollout_proc7
+[2023-09-12 06:25:30,332][31355] Starting seed is not provided
+[2023-09-12 06:25:30,332][31355] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:25:30,332][31355] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:25:30,333][31355] RunningMeanStd input shape: (23,)
+[2023-09-12 06:25:30,333][31355] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:25:30,334][31355] RunningMeanStd input shape: (1,)
+[2023-09-12 06:25:30,354][31355] ConvEncoder: input_channels=3
+[2023-09-12 06:25:30,590][31355] Conv encoder output size: 512
+[2023-09-12 06:25:30,592][31355] Policy head output size: 640
+[2023-09-12 06:25:30,641][31355] Created Actor Critic model with architecture:
+[2023-09-12 06:25:30,641][31355] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (measurements): RunningMeanStdInPlace()
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+    (measurements_head): Sequential(
+      (0): Linear(in_features=23, out_features=128, bias=True)
+      (1): ELU(alpha=1.0)
+      (2): Linear(in_features=128, out_features=128, bias=True)
+      (3): ELU(alpha=1.0)
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(640, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=15, bias=True)
+  )
+)
+[2023-09-12 06:25:32,001][31355] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:25:32,002][31355] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-12 06:25:32,042][31355] Loading model from checkpoint
+[2023-09-12 06:25:32,044][31355] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	Missing key(s) in state_dict: "obs_normalizer.running_mean_std.running_mean_std.measurements.running_mean", "obs_normalizer.running_mean_std.running_mean_std.measurements.running_var", "obs_normalizer.running_mean_std.running_mean_std.measurements.count", "encoder.measurements_head.0.weight", "encoder.measurements_head.0.bias", "encoder.measurements_head.2.weight", "encoder.measurements_head.2.bias". 
+	size mismatch for core.core.weight_ih_l0: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1536, 640]).
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([15, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([15]).
+[2023-09-12 06:25:32,045][31355] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	Missing key(s) in state_dict: "obs_normalizer.running_mean_std.running_mean_std.measurements.running_mean", "obs_normalizer.running_mean_std.running_mean_std.measurements.running_var", "obs_normalizer.running_mean_std.running_mean_std.measurements.count", "encoder.measurements_head.0.weight", "encoder.measurements_head.0.bias", "encoder.measurements_head.2.weight", "encoder.measurements_head.2.bias". 
+	size mismatch for core.core.weight_ih_l0: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1536, 640]).
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([15, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([15]). in evt loop learner_proc0_evt_loop
+[2023-09-12 06:25:32,408][31461] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:25:32,408][31461] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:25:32,423][31501] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:25:32,434][31500] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:25:32,453][31461] Num visible devices: 1
+[2023-09-12 06:25:32,524][31462] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:25:32,535][31466] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:25:32,540][31463] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:25:32,557][31499] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:25:32,581][31464] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:25:32,804][31465] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:25:48,621][88175] Heartbeat connected on Batcher_0
+[2023-09-12 06:25:48,628][88175] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 06:25:48,633][88175] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 06:25:48,635][88175] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 06:25:48,638][88175] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 06:25:48,642][88175] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 06:25:48,645][88175] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 06:25:48,648][88175] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 06:25:48,653][88175] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 06:25:48,655][88175] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 06:27:04,705][88175] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 88175], exiting...
+[2023-09-12 06:27:04,706][31464] Stopping RolloutWorker_w2...
+[2023-09-12 06:27:04,706][31499] Stopping RolloutWorker_w5...
+[2023-09-12 06:27:04,707][31462] Stopping RolloutWorker_w1...
+[2023-09-12 06:27:04,707][31500] Stopping RolloutWorker_w6...
+[2023-09-12 06:27:04,707][31465] Stopping RolloutWorker_w4...
+[2023-09-12 06:27:04,707][31464] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:27:04,707][31466] Stopping RolloutWorker_w3...
+[2023-09-12 06:27:04,707][31499] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:27:04,707][31501] Stopping RolloutWorker_w7...
+[2023-09-12 06:27:04,707][31463] Stopping RolloutWorker_w0...
+[2023-09-12 06:27:04,707][31462] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:27:04,707][31461] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:27:04,707][31500] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:27:04,707][31355] Stopping Batcher_0...
+[2023-09-12 06:27:04,707][31465] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:27:04,707][31466] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:27:04,706][88175] Runner profile tree view:
+main_loop: 96.0517
+[2023-09-12 06:27:04,707][31501] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:27:04,707][31463] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:27:04,708][31355] Loop batcher_evt_loop terminating...
+[2023-09-12 06:27:04,708][31461] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:27:04,708][88175] Collected {}, FPS: 0.0
+[2023-09-12 06:27:29,860][88175] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:27:29,862][88175] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:27:29,863][88175] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:27:29,864][88175] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:27:29,866][88175] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:27:29,867][88175] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:27:29,868][88175] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:27:29,869][88175] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:27:29,869][88175] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:27:29,870][88175] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:27:29,871][88175] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:27:29,872][88175] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:27:29,872][88175] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:27:29,873][88175] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:27:29,874][88175] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:27:29,875][88175] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:27:29,875][88175] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:27:29,876][88175] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:27:29,878][88175] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:27:29,879][88175] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:27:29,880][88175] register_encoder_factory: <function make_vizdoom_encoder at 0x7fdf25a3f5b0>
+[2023-09-12 06:27:29,922][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:27:29,923][88175] Overriding arg 'env' with value 'doom_basic' passed from command line
+[2023-09-12 06:27:29,925][88175] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:27:29,926][88175] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:27:29,927][88175] Weights and Biases integration disabled
+[2023-09-12 06:27:29,930][88175] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:27:31,960][88175] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_basic
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:27:31,962][88175] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:27:32,882][88175] Rollout worker 0 uses device cpu
+[2023-09-12 06:27:32,884][88175] Rollout worker 1 uses device cpu
+[2023-09-12 06:27:32,886][88175] Rollout worker 2 uses device cpu
+[2023-09-12 06:27:32,887][88175] Rollout worker 3 uses device cpu
+[2023-09-12 06:27:32,889][88175] Rollout worker 4 uses device cpu
+[2023-09-12 06:27:32,890][88175] Rollout worker 5 uses device cpu
+[2023-09-12 06:27:32,891][88175] Rollout worker 6 uses device cpu
+[2023-09-12 06:27:32,892][88175] Rollout worker 7 uses device cpu
+[2023-09-12 06:27:32,921][88175] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:27:32,922][88175] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:27:32,949][88175] Starting all processes...
+[2023-09-12 06:27:32,950][88175] Starting process learner_proc0
+[2023-09-12 06:27:34,566][88175] Starting all processes...
+[2023-09-12 06:27:34,568][38459] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:27:34,568][38459] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:27:34,572][88175] Starting process inference_proc0-0
+[2023-09-12 06:27:34,573][88175] Starting process rollout_proc0
+[2023-09-12 06:27:34,573][88175] Starting process rollout_proc1
+[2023-09-12 06:27:34,574][88175] Starting process rollout_proc2
+[2023-09-12 06:27:34,574][88175] Starting process rollout_proc3
+[2023-09-12 06:27:34,575][88175] Starting process rollout_proc4
+[2023-09-12 06:27:34,606][38459] Num visible devices: 1
+[2023-09-12 06:27:34,575][88175] Starting process rollout_proc5
+[2023-09-12 06:27:34,576][88175] Starting process rollout_proc6
+[2023-09-12 06:27:34,576][88175] Starting process rollout_proc7
+[2023-09-12 06:27:34,645][38459] Starting seed is not provided
+[2023-09-12 06:27:34,646][38459] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:27:34,646][38459] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:27:34,646][38459] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:27:34,647][38459] RunningMeanStd input shape: (1,)
+[2023-09-12 06:27:34,660][38459] ConvEncoder: input_channels=3
+[2023-09-12 06:27:34,868][38459] Conv encoder output size: 512
+[2023-09-12 06:27:34,868][38459] Policy head output size: 512
+[2023-09-12 06:27:34,883][38459] Created Actor Critic model with architecture:
+[2023-09-12 06:27:34,884][38459] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(512, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=4, bias=True)
+  )
+)
+[2023-09-12 06:27:36,099][38459] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:27:36,099][38459] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-12 06:27:36,136][38459] Loading model from checkpoint
+[2023-09-12 06:27:36,137][38459] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([4, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([4]).
+[2023-09-12 06:27:36,138][38459] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([4, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([4]). in evt loop learner_proc0_evt_loop
+[2023-09-12 06:27:36,561][38587] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:27:36,574][38632] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:27:36,630][38589] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:27:36,771][38591] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:27:36,783][38590] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:27:36,850][38586] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:27:36,850][38586] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:27:36,850][38627] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:27:36,851][38634] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:27:36,853][38588] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:27:36,868][38586] Num visible devices: 1
+[2023-09-12 06:27:52,915][88175] Heartbeat connected on Batcher_0
+[2023-09-12 06:27:52,922][88175] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 06:27:52,927][88175] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 06:27:52,930][88175] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 06:27:52,933][88175] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 06:27:52,937][88175] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 06:27:52,940][88175] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 06:27:52,942][88175] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 06:27:52,946][88175] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 06:27:52,949][88175] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 06:28:08,799][88175] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 88175], exiting...
+[2023-09-12 06:28:08,800][38632] Stopping RolloutWorker_w6...
+[2023-09-12 06:28:08,801][38589] Stopping RolloutWorker_w3...
+[2023-09-12 06:28:08,801][38590] Stopping RolloutWorker_w4...
+[2023-09-12 06:28:08,801][38588] Stopping RolloutWorker_w2...
+[2023-09-12 06:28:08,801][38632] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:28:08,801][38627] Stopping RolloutWorker_w5...
+[2023-09-12 06:28:08,801][38591] Stopping RolloutWorker_w1...
+[2023-09-12 06:28:08,801][38634] Stopping RolloutWorker_w7...
+[2023-09-12 06:28:08,801][38589] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:28:08,801][38586] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:28:08,801][38459] Stopping Batcher_0...
+[2023-09-12 06:28:08,801][38588] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:28:08,801][38590] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:28:08,801][38634] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:28:08,800][88175] Runner profile tree view:
+main_loop: 35.8515
+[2023-09-12 06:28:08,802][38586] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:28:08,801][38591] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:28:08,801][38627] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:28:08,802][38459] Loop batcher_evt_loop terminating...
+[2023-09-12 06:28:08,802][38587] Stopping RolloutWorker_w0...
+[2023-09-12 06:28:08,802][38587] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:28:08,802][88175] Collected {}, FPS: 0.0
+[2023-09-12 06:28:13,394][88175] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:28:13,396][88175] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:28:13,397][88175] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:28:13,399][88175] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:28:13,400][88175] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:28:13,401][88175] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:28:13,402][88175] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:28:13,403][88175] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:28:13,404][88175] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:28:13,405][88175] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:28:13,406][88175] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:28:13,406][88175] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:28:13,407][88175] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:28:13,407][88175] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:28:13,409][88175] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:28:13,409][88175] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:28:13,410][88175] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:28:13,411][88175] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:28:13,412][88175] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:28:13,412][88175] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:28:13,413][88175] register_encoder_factory: <function make_vizdoom_encoder at 0x7fdf25a3f5b0>
+[2023-09-12 06:28:13,438][88175] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:28:13,439][88175] Overriding arg 'env' with value 'doom_dm' passed from command line
+[2023-09-12 06:28:13,443][88175] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:28:13,444][88175] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:28:13,444][88175] Weights and Biases integration disabled
+[2023-09-12 06:28:13,447][88175] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:28:15,633][88175] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_dm
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:28:15,635][88175] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:28:16,555][88175] Rollout worker 0 uses device cpu
+[2023-09-12 06:28:16,557][88175] Rollout worker 1 uses device cpu
+[2023-09-12 06:28:16,558][88175] Rollout worker 2 uses device cpu
+[2023-09-12 06:28:16,559][88175] Rollout worker 3 uses device cpu
+[2023-09-12 06:28:16,560][88175] Rollout worker 4 uses device cpu
+[2023-09-12 06:28:16,561][88175] Rollout worker 5 uses device cpu
+[2023-09-12 06:28:16,562][88175] Rollout worker 6 uses device cpu
+[2023-09-12 06:28:16,563][88175] Rollout worker 7 uses device cpu
+[2023-09-12 06:28:16,746][88175] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:28:16,747][88175] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:28:16,774][88175] Starting all processes...
+[2023-09-12 06:28:16,776][88175] Starting process learner_proc0
+[2023-09-12 06:28:18,388][88175] Starting all processes...
+[2023-09-12 06:28:18,389][41812] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:28:18,390][41812] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:28:18,392][88175] Starting process inference_proc0-0
+[2023-09-12 06:28:18,392][88175] Starting process rollout_proc0
+[2023-09-12 06:28:18,393][88175] Starting process rollout_proc1
+[2023-09-12 06:28:18,393][88175] Starting process rollout_proc2
+[2023-09-12 06:28:18,407][41812] Num visible devices: 1
+[2023-09-12 06:28:18,394][88175] Starting process rollout_proc3
+[2023-09-12 06:28:18,394][88175] Starting process rollout_proc4
+[2023-09-12 06:28:18,395][88175] Starting process rollout_proc5
+[2023-09-12 06:28:18,432][41812] Starting seed is not provided
+[2023-09-12 06:28:18,432][41812] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:28:18,432][41812] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:28:18,432][41812] RunningMeanStd input shape: (23,)
+[2023-09-12 06:28:18,433][41812] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:28:18,434][41812] RunningMeanStd input shape: (1,)
+[2023-09-12 06:28:18,396][88175] Starting process rollout_proc6
+[2023-09-12 06:28:18,452][41812] ConvEncoder: input_channels=3
+[2023-09-12 06:28:18,397][88175] Starting process rollout_proc7
+[2023-09-12 06:28:18,665][41812] Conv encoder output size: 512
+[2023-09-12 06:28:18,667][41812] Policy head output size: 640
+[2023-09-12 06:28:18,695][41812] Created Actor Critic model with architecture:
+[2023-09-12 06:28:18,696][41812] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (measurements): RunningMeanStdInPlace()
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+    (measurements_head): Sequential(
+      (0): Linear(in_features=23, out_features=128, bias=True)
+      (1): ELU(alpha=1.0)
+      (2): Linear(in_features=128, out_features=128, bias=True)
+      (3): ELU(alpha=1.0)
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(640, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=15, bias=True)
+  )
+)
+[2023-09-12 06:28:19,998][41812] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:28:19,999][41812] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-12 06:28:20,030][41812] Loading model from checkpoint
+[2023-09-12 06:28:20,031][41812] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	Missing key(s) in state_dict: "obs_normalizer.running_mean_std.running_mean_std.measurements.running_mean", "obs_normalizer.running_mean_std.running_mean_std.measurements.running_var", "obs_normalizer.running_mean_std.running_mean_std.measurements.count", "encoder.measurements_head.0.weight", "encoder.measurements_head.0.bias", "encoder.measurements_head.2.weight", "encoder.measurements_head.2.bias". 
+	size mismatch for core.core.weight_ih_l0: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1536, 640]).
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([15, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([15]).
+[2023-09-12 06:28:20,032][41812] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	Missing key(s) in state_dict: "obs_normalizer.running_mean_std.running_mean_std.measurements.running_mean", "obs_normalizer.running_mean_std.running_mean_std.measurements.running_var", "obs_normalizer.running_mean_std.running_mean_std.measurements.count", "encoder.measurements_head.0.weight", "encoder.measurements_head.0.bias", "encoder.measurements_head.2.weight", "encoder.measurements_head.2.bias". 
+	size mismatch for core.core.weight_ih_l0: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1536, 640]).
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([15, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([15]). in evt loop learner_proc0_evt_loop
+[2023-09-12 06:28:20,403][42087] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:28:20,538][42048] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:28:20,602][42004] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:28:20,602][42004] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:28:20,607][42084] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:28:20,613][42049] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:28:20,644][42083] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:28:20,646][42004] Num visible devices: 1
+[2023-09-12 06:28:20,683][42085] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:28:20,687][42089] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:28:20,702][42088] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:28:36,740][88175] Heartbeat connected on Batcher_0
+[2023-09-12 06:28:36,747][88175] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 06:28:36,752][88175] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 06:28:36,755][88175] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 06:28:36,758][88175] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 06:28:36,762][88175] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 06:28:36,765][88175] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 06:28:36,768][88175] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 06:28:36,771][88175] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 06:28:36,775][88175] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 06:34:38,125][88175] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 88175], exiting...
+[2023-09-12 06:34:38,127][42087] Stopping RolloutWorker_w1...
+[2023-09-12 06:34:38,127][42004] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:34:38,127][42088] Stopping RolloutWorker_w5...
+[2023-09-12 06:34:38,127][42083] Stopping RolloutWorker_w3...
+[2023-09-12 06:34:38,127][42048] Stopping RolloutWorker_w2...
+[2023-09-12 06:34:38,127][42084] Stopping RolloutWorker_w4...
+[2023-09-12 06:34:38,127][41812] Stopping Batcher_0...
+[2023-09-12 06:34:38,127][42087] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:34:38,127][42049] Stopping RolloutWorker_w0...
+[2023-09-12 06:34:38,127][42089] Stopping RolloutWorker_w7...
+[2023-09-12 06:34:38,127][42088] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:34:38,127][42085] Stopping RolloutWorker_w6...
+[2023-09-12 06:34:38,127][42004] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:34:38,128][42048] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:34:38,128][42083] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:34:38,128][42084] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:34:38,128][41812] Loop batcher_evt_loop terminating...
+[2023-09-12 06:34:38,128][42049] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:34:38,128][42089] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:34:38,128][42085] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:34:38,127][88175] Runner profile tree view:
+main_loop: 381.3526
+[2023-09-12 06:34:38,128][88175] Collected {}, FPS: 0.0
+[2023-09-12 06:34:57,628][64313] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:34:58,549][64313] Rollout worker 0 uses device cpu
+[2023-09-12 06:34:58,551][64313] Rollout worker 1 uses device cpu
+[2023-09-12 06:34:58,552][64313] Rollout worker 2 uses device cpu
+[2023-09-12 06:34:58,552][64313] Rollout worker 3 uses device cpu
+[2023-09-12 06:34:58,553][64313] Rollout worker 4 uses device cpu
+[2023-09-12 06:34:58,554][64313] Rollout worker 5 uses device cpu
+[2023-09-12 06:34:58,555][64313] Rollout worker 6 uses device cpu
+[2023-09-12 06:34:58,556][64313] Rollout worker 7 uses device cpu
+[2023-09-12 06:34:58,777][64313] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:34:58,779][64313] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:34:58,805][64313] Starting all processes...
+[2023-09-12 06:34:58,807][64313] Starting process learner_proc0
+[2023-09-12 06:35:00,456][64313] Starting all processes...
+[2023-09-12 06:35:00,458][64995] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:35:00,458][64995] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:35:00,463][64313] Starting process inference_proc0-0
+[2023-09-12 06:35:00,463][64313] Starting process rollout_proc0
+[2023-09-12 06:35:00,464][64313] Starting process rollout_proc1
+[2023-09-12 06:35:00,464][64313] Starting process rollout_proc2
+[2023-09-12 06:35:00,465][64313] Starting process rollout_proc3
+[2023-09-12 06:35:00,466][64313] Starting process rollout_proc4
+[2023-09-12 06:35:00,503][64995] Num visible devices: 1
+[2023-09-12 06:35:00,542][64995] Starting seed is not provided
+[2023-09-12 06:35:00,543][64995] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:35:00,543][64995] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:35:00,543][64995] RunningMeanStd input shape: (23,)
+[2023-09-12 06:35:00,544][64995] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:35:00,544][64995] RunningMeanStd input shape: (1,)
+[2023-09-12 06:35:00,466][64313] Starting process rollout_proc5
+[2023-09-12 06:35:00,467][64313] Starting process rollout_proc6
+[2023-09-12 06:35:00,468][64313] Starting process rollout_proc7
+[2023-09-12 06:35:00,557][64995] ConvEncoder: input_channels=3
+[2023-09-12 06:35:00,783][64995] Conv encoder output size: 512
+[2023-09-12 06:35:00,784][64995] Policy head output size: 640
+[2023-09-12 06:35:00,801][64995] Created Actor Critic model with architecture:
+[2023-09-12 06:35:00,802][64995] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (measurements): RunningMeanStdInPlace()
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+    (measurements_head): Sequential(
+      (0): Linear(in_features=23, out_features=128, bias=True)
+      (1): ELU(alpha=1.0)
+      (2): Linear(in_features=128, out_features=128, bias=True)
+      (3): ELU(alpha=1.0)
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(640, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=15, bias=True)
+  )
+)
+[2023-09-12 06:35:02,194][64995] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:35:02,195][64995] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-12 06:35:02,240][64995] Loading model from checkpoint
+[2023-09-12 06:35:02,241][64995] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	Missing key(s) in state_dict: "obs_normalizer.running_mean_std.running_mean_std.measurements.running_mean", "obs_normalizer.running_mean_std.running_mean_std.measurements.running_var", "obs_normalizer.running_mean_std.running_mean_std.measurements.count", "encoder.measurements_head.0.weight", "encoder.measurements_head.0.bias", "encoder.measurements_head.2.weight", "encoder.measurements_head.2.bias". 
+	size mismatch for core.core.weight_ih_l0: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1536, 640]).
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([15, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([15]).
+[2023-09-12 06:35:02,242][64995] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	Missing key(s) in state_dict: "obs_normalizer.running_mean_std.running_mean_std.measurements.running_mean", "obs_normalizer.running_mean_std.running_mean_std.measurements.running_var", "obs_normalizer.running_mean_std.running_mean_std.measurements.count", "encoder.measurements_head.0.weight", "encoder.measurements_head.0.bias", "encoder.measurements_head.2.weight", "encoder.measurements_head.2.bias". 
+	size mismatch for core.core.weight_ih_l0: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1536, 640]).
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([15, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([15]). in evt loop learner_proc0_evt_loop
+[2023-09-12 06:35:02,611][65114] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:35:02,629][65151] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:35:02,629][65082] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:35:02,657][65152] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:35:02,724][65116] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:35:02,755][65153] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:35:02,785][65081] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:35:02,786][65081] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:35:02,793][65119] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:35:02,803][65081] Num visible devices: 1
+[2023-09-12 06:35:02,938][65154] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:35:18,771][64313] Heartbeat connected on Batcher_0
+[2023-09-12 06:35:18,778][64313] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 06:35:18,785][64313] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 06:35:18,788][64313] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 06:35:18,791][64313] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 06:35:18,793][64313] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 06:35:18,796][64313] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 06:35:18,799][64313] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 06:35:18,802][64313] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 06:35:18,805][64313] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 06:36:00,305][64313] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 64313], exiting...
+[2023-09-12 06:36:00,307][65081] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:36:00,307][65119] Stopping RolloutWorker_w4...
+[2023-09-12 06:36:00,308][65153] Stopping RolloutWorker_w6...
+[2023-09-12 06:36:00,308][65116] Stopping RolloutWorker_w2...
+[2023-09-12 06:36:00,308][65082] Stopping RolloutWorker_w0...
+[2023-09-12 06:36:00,308][65151] Stopping RolloutWorker_w3...
+[2023-09-12 06:36:00,308][65081] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:36:00,308][65153] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:36:00,308][65119] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:36:00,308][65152] Stopping RolloutWorker_w5...
+[2023-09-12 06:36:00,308][65151] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:36:00,308][65116] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:36:00,308][65082] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:36:00,308][65114] Stopping RolloutWorker_w1...
+[2023-09-12 06:36:00,308][65154] Stopping RolloutWorker_w7...
+[2023-09-12 06:36:00,308][65152] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:36:00,308][64995] Stopping Batcher_0...
+[2023-09-12 06:36:00,308][65114] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:36:00,309][65154] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:36:00,309][64995] Loop batcher_evt_loop terminating...
+[2023-09-12 06:36:00,308][64313] Runner profile tree view:
+main_loop: 61.5025
+[2023-09-12 06:36:00,310][64313] Collected {}, FPS: 0.0
+[2023-09-12 06:36:00,602][64313] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:36:00,604][64313] Overriding arg 'num_workers' with value 1 passed from command line
+[2023-09-12 06:36:00,605][64313] Adding new argument 'no_render'=True that is not in the saved config file!
+[2023-09-12 06:36:00,606][64313] Adding new argument 'save_video'=True that is not in the saved config file!
+[2023-09-12 06:36:00,606][64313] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
+[2023-09-12 06:36:00,607][64313] Adding new argument 'video_name'=None that is not in the saved config file!
+[2023-09-12 06:36:00,608][64313] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file!
+[2023-09-12 06:36:00,609][64313] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
+[2023-09-12 06:36:00,611][64313] Adding new argument 'push_to_hub'=False that is not in the saved config file!
+[2023-09-12 06:36:00,612][64313] Adding new argument 'hf_repository'=None that is not in the saved config file!
+[2023-09-12 06:36:00,613][64313] Adding new argument 'policy_index'=0 that is not in the saved config file!
+[2023-09-12 06:36:00,614][64313] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
+[2023-09-12 06:36:00,615][64313] Adding new argument 'train_script'=None that is not in the saved config file!
+[2023-09-12 06:36:00,615][64313] Adding new argument 'enjoy_script'=None that is not in the saved config file!
+[2023-09-12 06:36:00,616][64313] Using frameskip 1 and render_action_repeat=4 for evaluation
+[2023-09-12 06:36:00,619][64313] Multi agent env, num agents: 8
+[2023-09-12 06:36:00,659][64313] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-12 06:36:00,664][64313] RunningMeanStd input shape: (23,)
+[2023-09-12 06:36:00,666][64313] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:36:00,667][64313] RunningMeanStd input shape: (1,)
+[2023-09-12 06:36:00,688][64313] ConvEncoder: input_channels=3
+[2023-09-12 06:36:00,890][64313] Conv encoder output size: 512
+[2023-09-12 06:36:00,892][64313] Policy head output size: 640
+[2023-09-12 06:36:02,034][64313] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-12 06:36:46,388][64313] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:36:46,390][64313] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:36:46,393][64313] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:36:46,394][64313] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:36:46,396][64313] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:36:46,397][64313] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:36:46,398][64313] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:36:46,399][64313] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:36:46,399][64313] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:36:46,400][64313] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:36:46,401][64313] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:36:46,402][64313] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:36:46,402][64313] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:36:46,403][64313] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:36:46,404][64313] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:36:46,405][64313] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:36:46,406][64313] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:36:46,406][64313] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:36:46,407][64313] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:36:46,408][64313] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:36:46,408][64313] register_encoder_factory: <function make_vizdoom_encoder at 0x7f98c4f3beb0>
+[2023-09-12 06:36:46,430][64313] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:36:46,431][64313] Overriding arg 'env' with value 'defend_the_center' passed from command line
+[2023-09-12 06:36:46,435][64313] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:36:46,436][64313] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:36:46,437][64313] Weights and Biases integration disabled
+[2023-09-12 06:36:46,438][64313] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:37:04,898][64313] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:37:04,900][64313] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:37:04,902][64313] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:37:04,904][64313] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:37:04,906][64313] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:37:04,907][64313] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:37:04,910][64313] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:37:04,911][64313] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:37:04,913][64313] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:37:04,914][64313] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:37:04,915][64313] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:37:04,916][64313] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:37:04,917][64313] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:37:04,918][64313] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:37:04,918][64313] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:37:04,919][64313] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:37:04,920][64313] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:37:04,921][64313] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:37:04,921][64313] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:37:04,922][64313] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:37:04,922][64313] register_encoder_factory: <function make_vizdoom_encoder at 0x7f98c4f3beb0>
+[2023-09-12 06:37:04,945][64313] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:37:04,946][64313] Overriding arg 'env' with value 'doom_defend_the_center' passed from command line
+[2023-09-12 06:37:04,955][64313] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:37:04,956][64313] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:37:04,957][64313] Weights and Biases integration disabled
+[2023-09-12 06:37:04,960][64313] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:37:06,900][64313] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_defend_the_center
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:37:06,901][64313] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:37:07,873][64313] Rollout worker 0 uses device cpu
+[2023-09-12 06:37:07,874][64313] Rollout worker 1 uses device cpu
+[2023-09-12 06:37:07,876][64313] Rollout worker 2 uses device cpu
+[2023-09-12 06:37:07,878][64313] Rollout worker 3 uses device cpu
+[2023-09-12 06:37:07,879][64313] Rollout worker 4 uses device cpu
+[2023-09-12 06:37:07,881][64313] Rollout worker 5 uses device cpu
+[2023-09-12 06:37:07,882][64313] Rollout worker 6 uses device cpu
+[2023-09-12 06:37:07,884][64313] Rollout worker 7 uses device cpu
+[2023-09-12 06:37:07,947][64313] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:37:07,947][64313] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:37:07,973][64313] Starting all processes...
+[2023-09-12 06:37:07,975][64313] Starting process learner_proc0
+[2023-09-12 06:37:09,569][72190] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:37:09,569][72190] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:37:09,568][64313] Starting all processes...
+[2023-09-12 06:37:09,573][64313] Starting process inference_proc0-0
+[2023-09-12 06:37:09,573][64313] Starting process rollout_proc0
+[2023-09-12 06:37:09,574][64313] Starting process rollout_proc1
+[2023-09-12 06:37:09,574][64313] Starting process rollout_proc2
+[2023-09-12 06:37:09,575][64313] Starting process rollout_proc3
+[2023-09-12 06:37:09,576][64313] Starting process rollout_proc4
+[2023-09-12 06:37:09,577][64313] Starting process rollout_proc5
+[2023-09-12 06:37:09,613][72190] Num visible devices: 1
+[2023-09-12 06:37:09,577][64313] Starting process rollout_proc6
+[2023-09-12 06:37:09,578][64313] Starting process rollout_proc7
+[2023-09-12 06:37:09,653][72190] Starting seed is not provided
+[2023-09-12 06:37:09,653][72190] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:37:09,654][72190] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:37:09,654][72190] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:37:09,655][72190] RunningMeanStd input shape: (1,)
+[2023-09-12 06:37:09,670][72190] ConvEncoder: input_channels=3
+[2023-09-12 06:37:09,860][72190] Conv encoder output size: 512
+[2023-09-12 06:37:09,860][72190] Policy head output size: 512
+[2023-09-12 06:37:09,874][72190] Created Actor Critic model with architecture:
+[2023-09-12 06:37:09,874][72190] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(512, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=5, bias=True)
+  )
+)
+[2023-09-12 06:37:11,230][72190] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:37:11,231][72190] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009767_40005632.pth...
+[2023-09-12 06:37:11,268][72190] Loading model from checkpoint
+[2023-09-12 06:37:11,273][72190] Loaded experiment state at self.train_step=9767, self.env_steps=40005632
+[2023-09-12 06:37:11,274][72190] Initialized policy 0 weights for model version 9767
+[2023-09-12 06:37:11,276][72190] LearnerWorker_p0 finished initialization!
+[2023-09-12 06:37:11,276][72190] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:37:11,621][72330] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:37:11,676][72332] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:37:11,757][72334] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:37:11,770][72401] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:37:11,878][72403] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:37:11,900][72331] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:37:11,900][72331] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:37:11,919][72331] Num visible devices: 1
+[2023-09-12 06:37:12,023][72404] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:37:12,071][72402] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:37:12,130][72333] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:37:12,131][64313] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 40005632. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
+[2023-09-12 06:37:12,577][72331] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:37:12,578][72331] RunningMeanStd input shape: (1,)
+[2023-09-12 06:37:12,589][72331] ConvEncoder: input_channels=3
+[2023-09-12 06:37:12,691][72331] Conv encoder output size: 512
+[2023-09-12 06:37:12,691][72331] Policy head output size: 512
+[2023-09-12 06:37:12,979][64313] Inference worker 0-0 is ready!
+[2023-09-12 06:37:12,981][64313] All inference workers are ready! Signal rollout workers to start!
+[2023-09-12 06:37:13,018][72330] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-12 06:37:13,018][72403] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-12 06:37:13,019][72402] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-12 06:37:13,019][72333] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-12 06:37:13,037][72404] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-12 06:37:13,037][72334] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-12 06:37:13,038][72401] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-12 06:37:13,038][72332] Doom resolution: 160x120, resize resolution: (128, 72)
+[2023-09-12 06:37:13,329][72402] Decorrelating experience for 0 frames...
+[2023-09-12 06:37:13,330][72333] Decorrelating experience for 0 frames...
+[2023-09-12 06:37:13,332][72330] Decorrelating experience for 0 frames...
+[2023-09-12 06:37:13,353][72334] Decorrelating experience for 0 frames...
+[2023-09-12 06:37:13,373][72332] Decorrelating experience for 0 frames...
+[2023-09-12 06:37:13,608][72402] Decorrelating experience for 32 frames...
+[2023-09-12 06:37:13,613][72333] Decorrelating experience for 32 frames...
+[2023-09-12 06:37:13,615][72330] Decorrelating experience for 32 frames...
+[2023-09-12 06:37:13,628][72404] Decorrelating experience for 0 frames...
+[2023-09-12 06:37:13,638][72334] Decorrelating experience for 32 frames...
+[2023-09-12 06:37:13,645][72403] Decorrelating experience for 0 frames...
+[2023-09-12 06:37:13,899][72332] Decorrelating experience for 32 frames...
+[2023-09-12 06:37:13,918][72403] Decorrelating experience for 32 frames...
+[2023-09-12 06:37:13,927][72401] Decorrelating experience for 0 frames...
+[2023-09-12 06:37:13,986][72333] Decorrelating experience for 64 frames...
+[2023-09-12 06:37:14,001][72334] Decorrelating experience for 64 frames...
+[2023-09-12 06:37:14,223][72401] Decorrelating experience for 32 frames...
+[2023-09-12 06:37:14,226][72404] Decorrelating experience for 32 frames...
+[2023-09-12 06:37:14,267][72332] Decorrelating experience for 64 frames...
+[2023-09-12 06:37:14,348][72334] Decorrelating experience for 96 frames...
+[2023-09-12 06:37:14,351][72330] Decorrelating experience for 64 frames...
+[2023-09-12 06:37:14,353][72403] Decorrelating experience for 64 frames...
+[2023-09-12 06:37:14,615][72333] Decorrelating experience for 96 frames...
+[2023-09-12 06:37:14,619][72404] Decorrelating experience for 64 frames...
+[2023-09-12 06:37:14,729][72330] Decorrelating experience for 96 frames...
+[2023-09-12 06:37:14,729][72403] Decorrelating experience for 96 frames...
+[2023-09-12 06:37:14,737][72402] Decorrelating experience for 64 frames...
+[2023-09-12 06:37:14,909][72332] Decorrelating experience for 96 frames...
+[2023-09-12 06:37:14,961][64313] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 40005632. Throughput: 0: 0.0. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
+[2023-09-12 06:37:15,050][72404] Decorrelating experience for 96 frames...
+[2023-09-12 06:37:15,096][72401] Decorrelating experience for 64 frames...
+[2023-09-12 06:37:15,453][72402] Decorrelating experience for 96 frames...
+[2023-09-12 06:37:15,469][72401] Decorrelating experience for 96 frames...
+[2023-09-12 06:37:15,642][72190] Signal inference workers to stop experience collection...
+[2023-09-12 06:37:15,649][72331] InferenceWorker_p0-w0: stopping experience collection
+[2023-09-12 06:37:19,250][72190] Signal inference workers to resume experience collection...
+[2023-09-12 06:37:19,252][72190] Stopping Batcher_0...
+[2023-09-12 06:37:19,253][72190] Loop batcher_evt_loop terminating...
+[2023-09-12 06:37:19,261][64313] Component Batcher_0 stopped!
+[2023-09-12 06:37:19,266][72401] Stopping RolloutWorker_w5...
+[2023-09-12 06:37:19,266][72401] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:37:19,266][72332] Stopping RolloutWorker_w1...
+[2023-09-12 06:37:19,266][64313] Component RolloutWorker_w5 stopped!
+[2023-09-12 06:37:19,267][72332] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:37:19,267][72334] Stopping RolloutWorker_w3...
+[2023-09-12 06:37:19,267][72404] Stopping RolloutWorker_w7...
+[2023-09-12 06:37:19,267][72330] Stopping RolloutWorker_w0...
+[2023-09-12 06:37:19,267][72330] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:37:19,267][72404] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:37:19,267][72334] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:37:19,268][72403] Stopping RolloutWorker_w6...
+[2023-09-12 06:37:19,268][72403] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:37:19,267][64313] Component RolloutWorker_w1 stopped!
+[2023-09-12 06:37:19,269][64313] Component RolloutWorker_w3 stopped!
+[2023-09-12 06:37:19,270][64313] Component RolloutWorker_w7 stopped!
+[2023-09-12 06:37:19,271][72331] Weights refcount: 2 0
+[2023-09-12 06:37:19,271][64313] Component RolloutWorker_w0 stopped!
+[2023-09-12 06:37:19,272][72331] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:37:19,273][72331] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:37:19,273][64313] Component RolloutWorker_w6 stopped!
+[2023-09-12 06:37:19,274][64313] Component InferenceWorker_p0-w0 stopped!
+[2023-09-12 06:37:19,323][72402] Stopping RolloutWorker_w4...
+[2023-09-12 06:37:19,323][72333] Stopping RolloutWorker_w2...
+[2023-09-12 06:37:19,324][72402] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:37:19,324][72333] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:37:19,323][64313] Component RolloutWorker_w2 stopped!
+[2023-09-12 06:37:19,328][64313] Component RolloutWorker_w4 stopped!
+[2023-09-12 06:37:20,324][72190] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009769_40013824.pth...
+[2023-09-12 06:37:20,370][72190] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009556_39141376.pth
+[2023-09-12 06:37:20,377][72190] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009769_40013824.pth...
+[2023-09-12 06:37:20,432][72190] Stopping LearnerWorker_p0...
+[2023-09-12 06:37:20,432][72190] Loop learner_proc0_evt_loop terminating...
+[2023-09-12 06:37:20,432][64313] Component LearnerWorker_p0 stopped!
+[2023-09-12 06:37:20,434][64313] Waiting for process learner_proc0 to stop...
+[2023-09-12 06:37:21,055][64313] Waiting for process inference_proc0-0 to join...
+[2023-09-12 06:37:21,057][64313] Waiting for process rollout_proc0 to join...
+[2023-09-12 06:37:21,059][64313] Waiting for process rollout_proc1 to join...
+[2023-09-12 06:37:21,060][64313] Waiting for process rollout_proc2 to join...
+[2023-09-12 06:37:21,062][64313] Waiting for process rollout_proc3 to join...
+[2023-09-12 06:37:21,063][64313] Waiting for process rollout_proc4 to join...
+[2023-09-12 06:37:21,065][64313] Waiting for process rollout_proc5 to join...
+[2023-09-12 06:37:21,065][64313] Waiting for process rollout_proc6 to join...
+[2023-09-12 06:37:21,066][64313] Waiting for process rollout_proc7 to join...
+[2023-09-12 06:37:21,067][64313] Batcher 0 profile tree view:
+batching: 0.0290, releasing_batches: 0.0017
+[2023-09-12 06:37:21,068][64313] InferenceWorker_p0-w0 profile tree view:
+update_model: 0.0070
+wait_policy: 0.0000
+  wait_policy_total: 1.4687
+one_step: 0.0027
+  handle_policy_step: 1.1264
+    deserialize: 0.0288, stack: 0.0037, obs_to_device_normalize: 0.1309, forward: 0.8373, send_messages: 0.0336
+    prepare_outputs: 0.0644
+      to_cpu: 0.0333
+[2023-09-12 06:37:21,069][64313] Learner 0 profile tree view:
+misc: 0.0000, prepare_batch: 1.7902
+train: 3.1442
+  epoch_init: 0.0000, minibatch_init: 0.0000, losses_postprocess: 0.0004, kl_divergence: 0.0012, after_optimizer: 0.0050
+  calculate_losses: 0.3079
+    losses_init: 0.0000, forward_head: 0.2748, bptt_initial: 0.0154, tail: 0.0053, advantages_returns: 0.0009, losses: 0.0035
+    bptt: 0.0077
+      bptt_forward_core: 0.0076
+  update: 2.8288
+    clip: 0.0324
+[2023-09-12 06:37:21,070][64313] RolloutWorker_w0 profile tree view:
+wait_for_trajectories: 0.0008, enqueue_policy_requests: 0.0350, env_step: 0.4449, overhead: 0.0242, complete_rollouts: 0.0008
+save_policy_outputs: 0.0378
+  split_output_tensors: 0.0133
+[2023-09-12 06:37:21,071][64313] RolloutWorker_w7 profile tree view:
+wait_for_trajectories: 0.0005, enqueue_policy_requests: 0.0219, env_step: 0.3031, overhead: 0.0146, complete_rollouts: 0.0005
+save_policy_outputs: 0.0236
+  split_output_tensors: 0.0083
+[2023-09-12 06:37:21,072][64313] Loop Runner_EvtLoop terminating...
+[2023-09-12 06:37:21,073][64313] Runner profile tree view:
+main_loop: 13.1001
+[2023-09-12 06:37:21,074][64313] Collected {0: 40013824}, FPS: 625.3
+[2023-09-12 06:37:53,763][64313] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:37:53,767][64313] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:37:53,768][64313] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:37:53,769][64313] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:37:53,770][64313] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:37:53,771][64313] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:37:53,773][64313] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:37:53,773][64313] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:37:53,774][64313] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:37:53,776][64313] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:37:53,776][64313] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:37:53,777][64313] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:37:53,778][64313] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:37:53,781][64313] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:37:53,781][64313] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:37:53,782][64313] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:37:53,783][64313] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:37:53,784][64313] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:37:53,785][64313] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:37:53,786][64313] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:37:53,787][64313] register_encoder_factory: <function make_vizdoom_encoder at 0x7f98c4f3beb0>
+[2023-09-12 06:37:53,814][64313] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:37:53,815][64313] Overriding arg 'env' with value 'doom_basic' passed from command line
+[2023-09-12 06:37:53,819][64313] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:37:53,820][64313] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:37:53,821][64313] Weights and Biases integration disabled
+[2023-09-12 06:37:53,823][64313] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:37:55,920][64313] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_basic
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:37:55,923][64313] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:37:56,842][64313] Rollout worker 0 uses device cpu
+[2023-09-12 06:37:56,843][64313] Rollout worker 1 uses device cpu
+[2023-09-12 06:37:56,845][64313] Rollout worker 2 uses device cpu
+[2023-09-12 06:37:56,847][64313] Rollout worker 3 uses device cpu
+[2023-09-12 06:37:56,848][64313] Rollout worker 4 uses device cpu
+[2023-09-12 06:37:56,849][64313] Rollout worker 5 uses device cpu
+[2023-09-12 06:37:56,850][64313] Rollout worker 6 uses device cpu
+[2023-09-12 06:37:56,851][64313] Rollout worker 7 uses device cpu
+[2023-09-12 06:37:56,889][64313] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:37:56,890][64313] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:37:56,923][64313] Starting all processes...
+[2023-09-12 06:37:56,925][64313] Starting process learner_proc0
+[2023-09-12 06:37:58,625][64313] Starting all processes...
+[2023-09-12 06:37:58,627][76771] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:37:58,627][76771] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:37:58,631][64313] Starting process inference_proc0-0
+[2023-09-12 06:37:58,632][64313] Starting process rollout_proc0
+[2023-09-12 06:37:58,633][64313] Starting process rollout_proc1
+[2023-09-12 06:37:58,633][64313] Starting process rollout_proc2
+[2023-09-12 06:37:58,634][64313] Starting process rollout_proc3
+[2023-09-12 06:37:58,635][64313] Starting process rollout_proc4
+[2023-09-12 06:37:58,666][76771] Num visible devices: 1
+[2023-09-12 06:37:58,636][64313] Starting process rollout_proc5
+[2023-09-12 06:37:58,637][64313] Starting process rollout_proc6
+[2023-09-12 06:37:58,637][64313] Starting process rollout_proc7
+[2023-09-12 06:37:58,706][76771] Starting seed is not provided
+[2023-09-12 06:37:58,707][76771] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:37:58,707][76771] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:37:58,707][76771] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:37:58,708][76771] RunningMeanStd input shape: (1,)
+[2023-09-12 06:37:58,720][76771] ConvEncoder: input_channels=3
+[2023-09-12 06:37:58,937][76771] Conv encoder output size: 512
+[2023-09-12 06:37:58,938][76771] Policy head output size: 512
+[2023-09-12 06:37:58,952][76771] Created Actor Critic model with architecture:
+[2023-09-12 06:37:58,952][76771] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(512, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=4, bias=True)
+  )
+)
+[2023-09-12 06:38:00,366][76771] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:38:00,367][76771] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009769_40013824.pth...
+[2023-09-12 06:38:00,417][76771] Loading model from checkpoint
+[2023-09-12 06:38:00,419][76771] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([4, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([4]).
+[2023-09-12 06:38:00,420][76771] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([4, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([4]). in evt loop learner_proc0_evt_loop
+[2023-09-12 06:38:00,527][76958] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:38:00,531][76994] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:38:00,564][76956] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:38:00,571][76996] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:38:00,573][76961] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:38:00,676][76960] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:38:00,728][76959] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:38:00,769][76993] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:38:00,912][76957] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:38:00,912][76957] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:38:00,930][76957] Num visible devices: 1
+[2023-09-12 06:38:16,883][64313] Heartbeat connected on Batcher_0
+[2023-09-12 06:38:16,890][64313] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 06:38:16,895][64313] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 06:38:16,898][64313] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 06:38:16,902][64313] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 06:38:16,909][64313] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 06:38:16,912][64313] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 06:38:16,917][64313] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 06:38:16,920][64313] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 06:38:16,923][64313] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 06:38:34,673][64313] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 64313], exiting...
+[2023-09-12 06:38:34,675][76960] Stopping RolloutWorker_w3...
+[2023-09-12 06:38:34,675][76961] Stopping RolloutWorker_w4...
+[2023-09-12 06:38:34,675][76994] Stopping RolloutWorker_w6...
+[2023-09-12 06:38:34,675][76959] Stopping RolloutWorker_w2...
+[2023-09-12 06:38:34,675][76996] Stopping RolloutWorker_w7...
+[2023-09-12 06:38:34,675][76771] Stopping Batcher_0...
+[2023-09-12 06:38:34,675][76960] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:38:34,675][76958] Stopping RolloutWorker_w1...
+[2023-09-12 06:38:34,675][76993] Stopping RolloutWorker_w5...
+[2023-09-12 06:38:34,676][76959] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:38:34,676][76994] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:38:34,676][76996] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:38:34,676][76771] Loop batcher_evt_loop terminating...
+[2023-09-12 06:38:34,676][76961] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:38:34,675][76956] Stopping RolloutWorker_w0...
+[2023-09-12 06:38:34,676][76958] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:38:34,676][76993] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:38:34,675][76957] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:38:34,676][76956] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:38:34,676][76957] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:38:34,675][64313] Runner profile tree view:
+main_loop: 37.7520
+[2023-09-12 06:38:34,677][64313] Collected {}, FPS: 0.0
+[2023-09-12 06:38:37,772][64313] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:38:37,775][64313] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:38:37,779][64313] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:38:37,780][64313] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:38:37,782][64313] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:38:37,784][64313] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:38:37,785][64313] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:38:37,786][64313] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:38:37,787][64313] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:38:37,788][64313] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:38:37,790][64313] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:38:37,791][64313] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:38:37,792][64313] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:38:37,793][64313] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:38:37,794][64313] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:38:37,795][64313] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:38:37,795][64313] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:38:37,796][64313] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:38:37,797][64313] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:38:37,798][64313] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:38:37,799][64313] register_encoder_factory: <function make_vizdoom_encoder at 0x7f98c4f3beb0>
+[2023-09-12 06:38:37,823][64313] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:38:37,825][64313] Overriding arg 'env' with value 'doom_two_colors_easy' passed from command line
+[2023-09-12 06:38:37,834][64313] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:38:37,836][64313] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:38:37,837][64313] Weights and Biases integration disabled
+[2023-09-12 06:38:37,839][64313] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:38:39,863][64313] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_two_colors_easy
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:38:39,866][64313] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:38:40,786][64313] Rollout worker 0 uses device cpu
+[2023-09-12 06:38:40,787][64313] Rollout worker 1 uses device cpu
+[2023-09-12 06:38:40,788][64313] Rollout worker 2 uses device cpu
+[2023-09-12 06:38:40,788][64313] Rollout worker 3 uses device cpu
+[2023-09-12 06:38:40,789][64313] Rollout worker 4 uses device cpu
+[2023-09-12 06:38:40,790][64313] Rollout worker 5 uses device cpu
+[2023-09-12 06:38:40,791][64313] Rollout worker 6 uses device cpu
+[2023-09-12 06:38:40,792][64313] Rollout worker 7 uses device cpu
+[2023-09-12 06:38:40,829][64313] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:38:40,830][64313] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:38:40,857][64313] Starting all processes...
+[2023-09-12 06:38:40,858][64313] Starting process learner_proc0
+[2023-09-12 06:38:42,489][64313] Starting all processes...
+[2023-09-12 06:38:42,490][80300] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:38:42,491][80300] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:38:42,495][64313] Starting process inference_proc0-0
+[2023-09-12 06:38:42,495][64313] Starting process rollout_proc0
+[2023-09-12 06:38:42,496][64313] Starting process rollout_proc1
+[2023-09-12 06:38:42,496][64313] Starting process rollout_proc2
+[2023-09-12 06:38:42,497][64313] Starting process rollout_proc3
+[2023-09-12 06:38:42,497][64313] Starting process rollout_proc4
+[2023-09-12 06:38:42,498][64313] Starting process rollout_proc5
+[2023-09-12 06:38:42,536][80300] Num visible devices: 1
+[2023-09-12 06:38:42,498][64313] Starting process rollout_proc6
+[2023-09-12 06:38:42,499][64313] Starting process rollout_proc7
+[2023-09-12 06:38:42,596][80300] Starting seed is not provided
+[2023-09-12 06:38:42,597][80300] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:38:42,597][80300] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:38:42,597][80300] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:38:42,598][80300] RunningMeanStd input shape: (1,)
+[2023-09-12 06:38:42,617][80300] ConvEncoder: input_channels=3
+[2023-09-12 06:38:42,812][80300] Conv encoder output size: 512
+[2023-09-12 06:38:42,812][80300] Policy head output size: 512
+[2023-09-12 06:38:42,836][80300] Created Actor Critic model with architecture:
+[2023-09-12 06:38:42,836][80300] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(512, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=6, bias=True)
+  )
+)
+[2023-09-12 06:38:44,035][80300] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:38:44,035][80300] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009769_40013824.pth...
+[2023-09-12 06:38:44,078][80300] Loading model from checkpoint
+[2023-09-12 06:38:44,080][80300] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([6, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([6]).
+[2023-09-12 06:38:44,081][80300] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([6, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([6]). in evt loop learner_proc0_evt_loop
+[2023-09-12 06:38:44,408][80477] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:38:44,458][80475] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:38:44,495][80473] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:38:44,495][80473] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:38:44,507][80480] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:38:44,513][80473] Num visible devices: 1
+[2023-09-12 06:38:44,563][80474] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:38:44,607][80476] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:38:44,648][80512] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:38:44,827][80559] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:38:44,867][80479] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:39:00,823][64313] Heartbeat connected on Batcher_0
+[2023-09-12 06:39:00,830][64313] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 06:39:00,834][64313] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 06:39:00,838][64313] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 06:39:00,841][64313] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 06:39:00,844][64313] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 06:39:00,847][64313] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 06:39:00,850][64313] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 06:39:00,853][64313] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 06:39:00,856][64313] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 06:39:33,757][64313] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 64313], exiting...
+[2023-09-12 06:39:33,759][80474] Stopping RolloutWorker_w0...
+[2023-09-12 06:39:33,759][80559] Stopping RolloutWorker_w7...
+[2023-09-12 06:39:33,759][80473] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:39:33,759][80475] Stopping RolloutWorker_w1...
+[2023-09-12 06:39:33,759][80476] Stopping RolloutWorker_w3...
+[2023-09-12 06:39:33,759][80477] Stopping RolloutWorker_w2...
+[2023-09-12 06:39:33,759][80480] Stopping RolloutWorker_w4...
+[2023-09-12 06:39:33,759][80512] Stopping RolloutWorker_w6...
+[2023-09-12 06:39:33,760][80559] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:39:33,760][80474] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:39:33,759][80479] Stopping RolloutWorker_w5...
+[2023-09-12 06:39:33,760][80473] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:39:33,760][80300] Stopping Batcher_0...
+[2023-09-12 06:39:33,760][80477] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:39:33,760][80475] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:39:33,760][80480] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:39:33,760][80476] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:39:33,760][80512] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:39:33,760][80479] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:39:33,760][80300] Loop batcher_evt_loop terminating...
+[2023-09-12 06:39:33,759][64313] Runner profile tree view:
+main_loop: 52.9025
+[2023-09-12 06:39:33,763][64313] Collected {}, FPS: 0.0
+[2023-09-12 06:39:34,499][64313] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:39:34,501][64313] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:39:34,503][64313] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:39:34,505][64313] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:39:34,506][64313] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:39:34,506][64313] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:39:34,507][64313] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:39:34,508][64313] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:39:34,508][64313] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:39:34,509][64313] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:39:34,510][64313] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:39:34,510][64313] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:39:34,511][64313] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:39:34,512][64313] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:39:34,512][64313] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:39:34,513][64313] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:39:34,513][64313] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:39:34,514][64313] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:39:34,514][64313] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:39:34,515][64313] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:39:34,515][64313] register_encoder_factory: <function make_vizdoom_encoder at 0x7f98c4f3beb0>
+[2023-09-12 06:39:34,540][64313] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:39:34,542][64313] Overriding arg 'env' with value 'doom_dwango5' passed from command line
+[2023-09-12 06:39:34,547][64313] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:39:34,548][64313] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:39:34,549][64313] Weights and Biases integration disabled
+[2023-09-12 06:39:34,551][64313] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:39:36,574][64313] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_dwango5
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:39:36,578][64313] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:39:37,549][64313] Rollout worker 0 uses device cpu
+[2023-09-12 06:39:37,551][64313] Rollout worker 1 uses device cpu
+[2023-09-12 06:39:37,552][64313] Rollout worker 2 uses device cpu
+[2023-09-12 06:39:37,553][64313] Rollout worker 3 uses device cpu
+[2023-09-12 06:39:37,554][64313] Rollout worker 4 uses device cpu
+[2023-09-12 06:39:37,555][64313] Rollout worker 5 uses device cpu
+[2023-09-12 06:39:37,556][64313] Rollout worker 6 uses device cpu
+[2023-09-12 06:39:37,557][64313] Rollout worker 7 uses device cpu
+[2023-09-12 06:39:37,731][64313] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:39:37,732][64313] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:39:37,759][64313] Starting all processes...
+[2023-09-12 06:39:37,760][64313] Starting process learner_proc0
+[2023-09-12 06:39:39,372][64313] Starting all processes...
+[2023-09-12 06:39:39,373][84270] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:39:39,374][84270] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:39:39,377][64313] Starting process inference_proc0-0
+[2023-09-12 06:39:39,377][64313] Starting process rollout_proc0
+[2023-09-12 06:39:39,378][64313] Starting process rollout_proc1
+[2023-09-12 06:39:39,378][64313] Starting process rollout_proc2
+[2023-09-12 06:39:39,379][64313] Starting process rollout_proc3
+[2023-09-12 06:39:39,379][64313] Starting process rollout_proc4
+[2023-09-12 06:39:39,410][84270] Num visible devices: 1
+[2023-09-12 06:39:39,380][64313] Starting process rollout_proc5
+[2023-09-12 06:39:39,380][64313] Starting process rollout_proc6
+[2023-09-12 06:39:39,381][64313] Starting process rollout_proc7
+[2023-09-12 06:39:39,459][84270] Starting seed is not provided
+[2023-09-12 06:39:39,460][84270] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:39:39,460][84270] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:39:39,460][84270] RunningMeanStd input shape: (23,)
+[2023-09-12 06:39:39,461][84270] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:39:39,462][84270] RunningMeanStd input shape: (1,)
+[2023-09-12 06:39:39,485][84270] ConvEncoder: input_channels=3
+[2023-09-12 06:39:39,728][84270] Conv encoder output size: 512
+[2023-09-12 06:39:39,729][84270] Policy head output size: 640
+[2023-09-12 06:39:39,758][84270] Created Actor Critic model with architecture:
+[2023-09-12 06:39:39,758][84270] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (measurements): RunningMeanStdInPlace()
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+    (measurements_head): Sequential(
+      (0): Linear(in_features=23, out_features=128, bias=True)
+      (1): ELU(alpha=1.0)
+      (2): Linear(in_features=128, out_features=128, bias=True)
+      (3): ELU(alpha=1.0)
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(640, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=15, bias=True)
+  )
+)
+[2023-09-12 06:39:40,820][84270] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:39:40,821][84270] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009769_40013824.pth...
+[2023-09-12 06:39:40,861][84270] Loading model from checkpoint
+[2023-09-12 06:39:40,862][84270] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	Missing key(s) in state_dict: "obs_normalizer.running_mean_std.running_mean_std.measurements.running_mean", "obs_normalizer.running_mean_std.running_mean_std.measurements.running_var", "obs_normalizer.running_mean_std.running_mean_std.measurements.count", "encoder.measurements_head.0.weight", "encoder.measurements_head.0.bias", "encoder.measurements_head.2.weight", "encoder.measurements_head.2.bias". 
+	size mismatch for core.core.weight_ih_l0: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1536, 640]).
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([15, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([15]).
+[2023-09-12 06:39:40,863][84270] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	Missing key(s) in state_dict: "obs_normalizer.running_mean_std.running_mean_std.measurements.running_mean", "obs_normalizer.running_mean_std.running_mean_std.measurements.running_var", "obs_normalizer.running_mean_std.running_mean_std.measurements.count", "encoder.measurements_head.0.weight", "encoder.measurements_head.0.bias", "encoder.measurements_head.2.weight", "encoder.measurements_head.2.bias". 
+	size mismatch for core.core.weight_ih_l0: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1536, 640]).
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([15, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([15]). in evt loop learner_proc0_evt_loop
+[2023-09-12 06:39:41,349][84424] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:39:41,349][84385] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:39:41,351][84421] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:39:41,395][84387] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:39:41,399][84389] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:39:41,443][84423] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:39:41,451][84384] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:39:41,452][84384] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:39:41,471][84384] Num visible devices: 1
+[2023-09-12 06:39:41,507][84386] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:39:41,518][84388] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:39:57,726][64313] Heartbeat connected on Batcher_0
+[2023-09-12 06:39:57,732][64313] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 06:39:57,737][64313] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 06:39:57,740][64313] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 06:39:57,743][64313] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 06:39:57,746][64313] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 06:39:57,750][64313] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 06:39:57,753][64313] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 06:39:57,756][64313] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 06:39:57,759][64313] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 06:40:43,509][64313] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 64313], exiting...
+[2023-09-12 06:40:43,511][84384] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:40:43,511][84386] Stopping RolloutWorker_w3...
+[2023-09-12 06:40:43,510][84389] Stopping RolloutWorker_w4...
+[2023-09-12 06:40:43,511][84384] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:40:43,511][84387] Stopping RolloutWorker_w1...
+[2023-09-12 06:40:43,511][84421] Stopping RolloutWorker_w5...
+[2023-09-12 06:40:43,511][84424] Stopping RolloutWorker_w6...
+[2023-09-12 06:40:43,511][84423] Stopping RolloutWorker_w7...
+[2023-09-12 06:40:43,511][84388] Stopping RolloutWorker_w2...
+[2023-09-12 06:40:43,511][84385] Stopping RolloutWorker_w0...
+[2023-09-12 06:40:43,511][84270] Stopping Batcher_0...
+[2023-09-12 06:40:43,511][84386] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:40:43,510][64313] Runner profile tree view:
+main_loop: 65.7519
+[2023-09-12 06:40:43,511][84387] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:40:43,511][84389] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:40:43,511][84421] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:40:43,511][84423] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:40:43,511][84270] Loop batcher_evt_loop terminating...
+[2023-09-12 06:40:43,511][84385] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:40:43,511][84424] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:40:43,511][84388] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:40:43,511][64313] Collected {}, FPS: 0.0
+[2023-09-12 06:40:46,065][64313] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:40:46,067][64313] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:40:46,068][64313] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:40:46,069][64313] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:40:46,070][64313] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:40:46,071][64313] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:40:46,072][64313] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:40:46,073][64313] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:40:46,074][64313] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:40:46,075][64313] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:40:46,076][64313] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:40:46,076][64313] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:40:46,077][64313] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:40:46,078][64313] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:40:46,079][64313] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:40:46,080][64313] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:40:46,081][64313] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:40:46,082][64313] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:40:46,082][64313] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:40:46,083][64313] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:40:46,084][64313] register_encoder_factory: <function make_vizdoom_encoder at 0x7f98c4f3beb0>
+[2023-09-12 06:40:46,108][64313] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:40:46,110][64313] Overriding arg 'env' with value 'doom_deadly_corridor' passed from command line
+[2023-09-12 06:40:46,114][64313] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:40:46,116][64313] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:40:46,116][64313] Weights and Biases integration disabled
+[2023-09-12 06:40:46,118][64313] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:40:48,128][64313] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_deadly_corridor
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:40:48,131][64313] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
+[2023-09-12 06:40:49,053][64313] Rollout worker 0 uses device cpu
+[2023-09-12 06:40:49,055][64313] Rollout worker 1 uses device cpu
+[2023-09-12 06:40:49,056][64313] Rollout worker 2 uses device cpu
+[2023-09-12 06:40:49,058][64313] Rollout worker 3 uses device cpu
+[2023-09-12 06:40:49,059][64313] Rollout worker 4 uses device cpu
+[2023-09-12 06:40:49,060][64313] Rollout worker 5 uses device cpu
+[2023-09-12 06:40:49,061][64313] Rollout worker 6 uses device cpu
+[2023-09-12 06:40:49,062][64313] Rollout worker 7 uses device cpu
+[2023-09-12 06:40:49,100][64313] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:40:49,101][64313] InferenceWorker_p0-w0: min num requests: 2
+[2023-09-12 06:40:49,135][64313] Starting all processes...
+[2023-09-12 06:40:49,137][64313] Starting process learner_proc0
+[2023-09-12 06:40:50,749][64313] Starting all processes...
+[2023-09-12 06:40:50,751][89170] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:40:50,751][89170] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
+[2023-09-12 06:40:50,754][64313] Starting process inference_proc0-0
+[2023-09-12 06:40:50,755][64313] Starting process rollout_proc0
+[2023-09-12 06:40:50,756][64313] Starting process rollout_proc1
+[2023-09-12 06:40:50,792][89170] Num visible devices: 1
+[2023-09-12 06:40:50,756][64313] Starting process rollout_proc2
+[2023-09-12 06:40:50,756][64313] Starting process rollout_proc3
+[2023-09-12 06:40:50,835][89170] Starting seed is not provided
+[2023-09-12 06:40:50,836][89170] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:40:50,836][89170] Initializing actor-critic model on device cuda:0
+[2023-09-12 06:40:50,836][89170] RunningMeanStd input shape: (3, 72, 128)
+[2023-09-12 06:40:50,837][89170] RunningMeanStd input shape: (1,)
+[2023-09-12 06:40:50,756][64313] Starting process rollout_proc4
+[2023-09-12 06:40:50,757][64313] Starting process rollout_proc5
+[2023-09-12 06:40:50,757][64313] Starting process rollout_proc6
+[2023-09-12 06:40:50,851][89170] ConvEncoder: input_channels=3
+[2023-09-12 06:40:50,757][64313] Starting process rollout_proc7
+[2023-09-12 06:40:51,053][89170] Conv encoder output size: 512
+[2023-09-12 06:40:51,054][89170] Policy head output size: 512
+[2023-09-12 06:40:51,068][89170] Created Actor Critic model with architecture:
+[2023-09-12 06:40:51,068][89170] ActorCriticSharedWeights(
+  (obs_normalizer): ObservationNormalizer(
+    (running_mean_std): RunningMeanStdDictInPlace(
+      (running_mean_std): ModuleDict(
+        (obs): RunningMeanStdInPlace()
+      )
+    )
+  )
+  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
+  (encoder): VizdoomEncoder(
+    (basic_encoder): ConvEncoder(
+      (enc): RecursiveScriptModule(
+        original_name=ConvEncoderImpl
+        (conv_head): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Conv2d)
+          (1): RecursiveScriptModule(original_name=ELU)
+          (2): RecursiveScriptModule(original_name=Conv2d)
+          (3): RecursiveScriptModule(original_name=ELU)
+          (4): RecursiveScriptModule(original_name=Conv2d)
+          (5): RecursiveScriptModule(original_name=ELU)
+        )
+        (mlp_layers): RecursiveScriptModule(
+          original_name=Sequential
+          (0): RecursiveScriptModule(original_name=Linear)
+          (1): RecursiveScriptModule(original_name=ELU)
+        )
+      )
+    )
+  )
+  (core): ModelCoreRNN(
+    (core): GRU(512, 512)
+  )
+  (decoder): MlpDecoder(
+    (mlp): Identity()
+  )
+  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
+  (action_parameterization): ActionParameterizationDefault(
+    (distribution_linear): Linear(in_features=512, out_features=11, bias=True)
+  )
+)
+[2023-09-12 06:40:52,305][89170] Using optimizer <class 'torch.optim.adam.Adam'>
+[2023-09-12 06:40:52,306][89170] Loading state from checkpoint /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000009769_40013824.pth...
+[2023-09-12 06:40:52,349][89170] Loading model from checkpoint
+[2023-09-12 06:40:52,350][89170] EvtLoop [learner_proc0_evt_loop, process=learner_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Runner_EvtLoop', signal_name='start'), args=()
+Traceback (most recent call last):
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
+    slot_callable(*args)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner_worker.py", line 139, in init
+    init_model_data = self.learner.init()
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 245, in init
+    self.load_from_checkpoint(self.policy_id)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 307, in load_from_checkpoint
+    self._load_state(checkpoint_dict, load_progress=load_progress)
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/sample_factory/algo/learning/learner.py", line 291, in _load_state
+    self.actor_critic.load_state_dict(checkpoint_dict["model"])
+  File "/home/cogstack/.local/share/virtualenvs/sample_factory--NQNquiM/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
+    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
+RuntimeError: Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([11, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([11]).
+[2023-09-12 06:40:52,351][89170] Unhandled exception Error(s) in loading state_dict for ActorCriticSharedWeights:
+	size mismatch for action_parameterization.distribution_linear.weight: copying a param with shape torch.Size([5, 512]) from checkpoint, the shape in current model is torch.Size([11, 512]).
+	size mismatch for action_parameterization.distribution_linear.bias: copying a param with shape torch.Size([5]) from checkpoint, the shape in current model is torch.Size([11]). in evt loop learner_proc0_evt_loop
+[2023-09-12 06:40:52,756][89373] Worker 3 uses CPU cores [12, 13, 14, 15]
+[2023-09-12 06:40:52,765][89374] Worker 5 uses CPU cores [20, 21, 22, 23]
+[2023-09-12 06:40:52,775][89369] Worker 1 uses CPU cores [4, 5, 6, 7]
+[2023-09-12 06:40:52,803][89372] Worker 2 uses CPU cores [8, 9, 10, 11]
+[2023-09-12 06:40:52,806][89375] Worker 4 uses CPU cores [16, 17, 18, 19]
+[2023-09-12 06:40:52,926][89376] Worker 6 uses CPU cores [24, 25, 26, 27]
+[2023-09-12 06:40:52,938][89412] Worker 7 uses CPU cores [28, 29, 30, 31]
+[2023-09-12 06:40:53,020][89324] Worker 0 uses CPU cores [0, 1, 2, 3]
+[2023-09-12 06:40:53,033][89371] Using GPUs [0] for process 0 (actually maps to GPUs [0])
+[2023-09-12 06:40:53,033][89371] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
+[2023-09-12 06:40:53,073][89371] Num visible devices: 1
+[2023-09-12 06:41:09,094][64313] Heartbeat connected on Batcher_0
+[2023-09-12 06:41:09,101][64313] Heartbeat connected on InferenceWorker_p0-w0
+[2023-09-12 06:41:09,106][64313] Heartbeat connected on RolloutWorker_w0
+[2023-09-12 06:41:09,110][64313] Heartbeat connected on RolloutWorker_w1
+[2023-09-12 06:41:09,115][64313] Heartbeat connected on RolloutWorker_w2
+[2023-09-12 06:41:09,119][64313] Heartbeat connected on RolloutWorker_w3
+[2023-09-12 06:41:09,123][64313] Heartbeat connected on RolloutWorker_w4
+[2023-09-12 06:41:09,127][64313] Heartbeat connected on RolloutWorker_w5
+[2023-09-12 06:41:09,131][64313] Heartbeat connected on RolloutWorker_w6
+[2023-09-12 06:41:09,135][64313] Heartbeat connected on RolloutWorker_w7
+[2023-09-12 06:41:28,485][64313] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 64313], exiting...
+[2023-09-12 06:41:28,488][89412] Stopping RolloutWorker_w7...
+[2023-09-12 06:41:28,488][89374] Stopping RolloutWorker_w5...
+[2023-09-12 06:41:28,488][89324] Stopping RolloutWorker_w0...
+[2023-09-12 06:41:28,488][89375] Stopping RolloutWorker_w4...
+[2023-09-12 06:41:28,488][89369] Stopping RolloutWorker_w1...
+[2023-09-12 06:41:28,488][89376] Stopping RolloutWorker_w6...
+[2023-09-12 06:41:28,488][89373] Stopping RolloutWorker_w3...
+[2023-09-12 06:41:28,488][89412] Loop rollout_proc7_evt_loop terminating...
+[2023-09-12 06:41:28,489][89324] Loop rollout_proc0_evt_loop terminating...
+[2023-09-12 06:41:28,488][89372] Stopping RolloutWorker_w2...
+[2023-09-12 06:41:28,488][89170] Stopping Batcher_0...
+[2023-09-12 06:41:28,489][89374] Loop rollout_proc5_evt_loop terminating...
+[2023-09-12 06:41:28,488][89371] Stopping InferenceWorker_p0-w0...
+[2023-09-12 06:41:28,489][89376] Loop rollout_proc6_evt_loop terminating...
+[2023-09-12 06:41:28,489][89375] Loop rollout_proc4_evt_loop terminating...
+[2023-09-12 06:41:28,489][89373] Loop rollout_proc3_evt_loop terminating...
+[2023-09-12 06:41:28,489][89369] Loop rollout_proc1_evt_loop terminating...
+[2023-09-12 06:41:28,489][89372] Loop rollout_proc2_evt_loop terminating...
+[2023-09-12 06:41:28,489][89170] Loop batcher_evt_loop terminating...
+[2023-09-12 06:41:28,489][89371] Loop inference_proc0-0_evt_loop terminating...
+[2023-09-12 06:41:28,488][64313] Runner profile tree view:
+main_loop: 39.3526
+[2023-09-12 06:41:28,491][64313] Collected {}, FPS: 0.0
+[2023-09-12 06:41:36,434][64313] Environment doom_basic already registered, overwriting...
+[2023-09-12 06:41:36,437][64313] Environment doom_two_colors_easy already registered, overwriting...
+[2023-09-12 06:41:36,438][64313] Environment doom_two_colors_hard already registered, overwriting...
+[2023-09-12 06:41:36,439][64313] Environment doom_dm already registered, overwriting...
+[2023-09-12 06:41:36,441][64313] Environment doom_dwango5 already registered, overwriting...
+[2023-09-12 06:41:36,442][64313] Environment doom_my_way_home_flat_actions already registered, overwriting...
+[2023-09-12 06:41:36,443][64313] Environment doom_defend_the_center_flat_actions already registered, overwriting...
+[2023-09-12 06:41:36,444][64313] Environment doom_my_way_home already registered, overwriting...
+[2023-09-12 06:41:36,445][64313] Environment doom_deadly_corridor already registered, overwriting...
+[2023-09-12 06:41:36,446][64313] Environment doom_defend_the_center already registered, overwriting...
+[2023-09-12 06:41:36,447][64313] Environment doom_defend_the_line already registered, overwriting...
+[2023-09-12 06:41:36,448][64313] Environment doom_health_gathering already registered, overwriting...
+[2023-09-12 06:41:36,448][64313] Environment doom_health_gathering_supreme already registered, overwriting...
+[2023-09-12 06:41:36,449][64313] Environment doom_battle already registered, overwriting...
+[2023-09-12 06:41:36,450][64313] Environment doom_battle2 already registered, overwriting...
+[2023-09-12 06:41:36,450][64313] Environment doom_duel_bots already registered, overwriting...
+[2023-09-12 06:41:36,451][64313] Environment doom_deathmatch_bots already registered, overwriting...
+[2023-09-12 06:41:36,452][64313] Environment doom_duel already registered, overwriting...
+[2023-09-12 06:41:36,453][64313] Environment doom_deathmatch_full already registered, overwriting...
+[2023-09-12 06:41:36,453][64313] Environment doom_benchmark already registered, overwriting...
+[2023-09-12 06:41:36,454][64313] register_encoder_factory: <function make_vizdoom_encoder at 0x7f98c4f3beb0>
+[2023-09-12 06:41:36,481][64313] Loading existing experiment configuration from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json
+[2023-09-12 06:41:36,482][64313] Overriding arg 'env' with value 'doom_defend_the_line' passed from command line
+[2023-09-12 06:41:36,488][64313] Experiment dir /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment already exists!
+[2023-09-12 06:41:36,489][64313] Resuming existing experiment from /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment...
+[2023-09-12 06:41:36,490][64313] Weights and Biases integration disabled
+[2023-09-12 06:41:36,493][64313] Environment var CUDA_VISIBLE_DEVICES is 0,1
+
+[2023-09-12 06:41:38,600][64313] Starting experiment with the following configuration:
+help=False
+algo=APPO
+env=doom_defend_the_line
+experiment=default_experiment
+train_dir=/home/cogstack/Documents/optuna/environments/sample_factory/train_dir
+restart_behavior=resume
+device=gpu
+seed=None
+num_policies=1
+async_rl=True
+serial_mode=False
+batched_sampling=False
+num_batches_to_accumulate=2
+worker_num_splits=2
+policy_workers_per_policy=1
+max_policy_lag=1000
+num_workers=8
+num_envs_per_worker=4
+batch_size=1024
+num_batches_per_epoch=1
+num_epochs=1
+rollout=32
+recurrence=32
+shuffle_minibatches=False
+gamma=0.99
+reward_scale=1.0
+reward_clip=1000.0
+value_bootstrap=False
+normalize_returns=True
+exploration_loss_coeff=0.001
+value_loss_coeff=0.5
+kl_loss_coeff=0.0
+exploration_loss=symmetric_kl
+gae_lambda=0.95
+ppo_clip_ratio=0.1
+ppo_clip_value=0.2
+with_vtrace=False
+vtrace_rho=1.0
+vtrace_c=1.0
+optimizer=adam
+adam_eps=1e-06
+adam_beta1=0.9
+adam_beta2=0.999
+max_grad_norm=4.0
+learning_rate=0.0001
+lr_schedule=constant
+lr_schedule_kl_threshold=0.008
+lr_adaptive_min=1e-06
+lr_adaptive_max=0.01
+obs_subtract_mean=0.0
+obs_scale=255.0
+normalize_input=True
+normalize_input_keys=None
+decorrelate_experience_max_seconds=0
+decorrelate_envs_on_one_worker=True
+actor_worker_gpus=[]
+set_workers_cpu_affinity=True
+force_envs_single_thread=False
+default_niceness=0
+log_to_file=True
+experiment_summaries_interval=10
+flush_summaries_interval=30
+stats_avg=100
+summaries_use_frameskip=True
+heartbeat_interval=20
+heartbeat_reporting_interval=600
+train_for_env_steps=4000000
+train_for_seconds=10000000000
+save_every_sec=120
+keep_checkpoints=2
+load_checkpoint_kind=latest
+save_milestones_sec=-1
+save_best_every_sec=5
+save_best_metric=reward
+save_best_after=100000
+benchmark=False
+encoder_mlp_layers=[512, 512]
+encoder_conv_architecture=convnet_simple
+encoder_conv_mlp_layers=[512]
+use_rnn=True
+rnn_size=512
+rnn_type=gru
+rnn_num_layers=1
+decoder_mlp_layers=[]
+nonlinearity=elu
+policy_initialization=orthogonal
+policy_init_gain=1.0
+actor_critic_share_weights=True
+adaptive_stddev=True
+continuous_tanh_scale=0.0
+initial_stddev=1.0
+use_env_info_cache=False
+env_gpu_actions=False
+env_gpu_observations=True
+env_frameskip=4
+env_framestack=1
+pixel_format=CHW
+use_record_episode_statistics=False
+with_wandb=False
+wandb_user=None
+wandb_project=sample_factory
+wandb_group=None
+wandb_job_type=SF
+wandb_tags=[]
+with_pbt=False
+pbt_mix_policies_in_one_env=True
+pbt_period_env_steps=5000000
+pbt_start_mutation=20000000
+pbt_replace_fraction=0.3
+pbt_mutation_rate=0.15
+pbt_replace_reward_gap=0.1
+pbt_replace_reward_gap_absolute=1e-06
+pbt_optimize_gamma=False
+pbt_target_objective=true_objective
+pbt_perturb_min=1.1
+pbt_perturb_max=1.5
+num_agents=-1
+num_humans=0
+num_bots=-1
+start_bot_difficulty=None
+timelimit=None
+res_w=128
+res_h=72
+wide_aspect_ratio=False
+eval_env_frameskip=1
+fps=35
+command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
+cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
+git_hash=b12d96985caa7a7552d0840afdd14065f56f9f9a
+git_repo_name=https://github.com/MattStammers/optuna.git
+[2023-09-12 06:41:38,601][64313] Saving configuration to /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/config.json...
diff --git a/environments/unity/ml-agents b/environments/unity/ml-agents
--- a/environments/unity/ml-agents
+++ b/environments/unity/ml-agents
@@ -1 +1 @@
-Subproject commit 8bcedabd808ffb7097f88b800fc92dea82dfd610
+Subproject commit 8bcedabd808ffb7097f88b800fc92dea82dfd610-dirty