Initial commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 5483.53 +/- 1474.79
+      value: 2492.52 +/- 1181.09
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -37,15 +37,21 @@ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
 
 ```
 # Download model and save it into the logs/ folder
-python -m utils.load_from_hub --algo sac --env seals/Walker2d-v0 -orga HumanCompatibleAI -f logs/
+python -m rl_zoo3.load_from_hub --algo sac --env seals/Walker2d-v0 -orga HumanCompatibleAI -f logs/
 python enjoy.py --algo sac --env seals/Walker2d-v0  -f logs/
 ```
 
+If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
+```
+python -m rl_zoo3.load_from_hub --algo sac --env seals/Walker2d-v0 -orga HumanCompatibleAI -f logs/
+rl_zoo3 enjoy --algo sac --env seals/Walker2d-v0  -f logs/
+```
+
 ## Training (with the RL Zoo)
 ```
 python train.py --algo sac --env seals/Walker2d-v0 -f logs/
 # Upload the model and generate video (when possible)
-python -m utils.push_to_hub --algo sac --env seals/Walker2d-v0 -f logs/ -orga HumanCompatibleAI
+python -m rl_zoo3.push_to_hub --algo sac --env seals/Walker2d-v0 -f logs/ -orga HumanCompatibleAI
 ```
 
 ## Hyperparameters
@@ -58,7 +64,9 @@ OrderedDict([('batch_size', 128),
              ('n_timesteps', 1000000.0),
              ('policy', 'MlpPolicy'),
              ('policy_kwargs',
-              'dict(net_arch=[400, 300], log_std_init=0.1955317469998743)'),
+              {'log_std_init': 0.1955317469998743,
+               'net_arch': [400, 300],
+               'use_sde': False}),
              ('tau', 0.02),
              ('train_freq', 1),
              ('normalize', False)])

args.yml

@@ -1,6 +1,8 @@
 !!python/object/apply:collections.OrderedDict
 - - - algo
     - sac
+  - - conf_file
+    - hyperparams/python/sac.py
   - - device
     - cpu
   - - env
@@ -16,7 +18,7 @@
   - - hyperparams
     - null
   - - log_folder
-    - seals_experts_wandb_oldpickle/seed_9/
+    - logs
   - - log_interval
     - -1
   - - max_total_trials
@@ -41,6 +43,8 @@
     - null
   - - optimize_hyperparameters
     - false
+  - - progress
+    - false
   - - pruner
     - median
   - - sampler
@@ -50,13 +54,13 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 9
+    - 5
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - runs/seals/Walker2d-v0__sac__9__1658867336
+    - runs/seals/Walker2d-v0__sac__5__1672507712
   - - track
     - true
   - - trained_agent
@@ -70,6 +74,8 @@
   - - verbose
     - 1
   - - wandb_entity
-    - null
+    - ernestum
   - - wandb_project_name
-    - seals-experts-oldpickle
+    - seals-experts-normalized
+  - - yaml_file
+    - null

config.yml

@@ -14,7 +14,11 @@
   - - policy
     - MlpPolicy
   - - policy_kwargs
-    - dict(net_arch=[400, 300], log_std_init=0.1955317469998743)
+    - log_std_init: 0.1955317469998743
+      net_arch:
+      - 400
+      - 300
+      use_sde: false
   - - tau
     - 0.02
   - - train_freq

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:25270b7045ff6072b4ae60879bc63412b8e64456209e33f512817f1b0868bb37
-size 1514202
+oid sha256:f1d6d32b7ac2c7937e297249eecdbba14be9b3dc1b809e1415eacc0583980fb6
+size 1373262

results.json

@@ -1 +1 @@
-{"mean_reward": 5483.5284099, "std_reward": 1474.7914335759533, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-27T17:07:09.427002"}
+{"mean_reward": 2492.5241772, "std_reward": 1181.0920613431326, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-02T11:00:28.709939"}

sac-seals-Walker2d-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9712739c0b31fa0ded5734064e57bb93cf474fdfe1c2e12285320e44f1827b35
-size 5806370
+oid sha256:42b9465decbc006ee3c68af4bd6e6166d0ac22a72cd30dd7bb7dab2e2bd80d95
+size 5806513

sac-seals-Walker2d-v0/_stable_baselines3_version

@@ -1 +1 @@
-1.6.0
+1.6.2

sac-seals-Walker2d-v0/actor.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8a5bc4b1a434075dbc54e465412dd6806ba1813b0a394785f158cdaf78639c1c
+oid sha256:1bcdfff6139b595523b30377aef80b9384a8ebb756b53fd10a3ba8542228d123
 size 1058141

sac-seals-Walker2d-v0/critic.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bb7e185fe43419961da95f886d57e530dbef3a774b46bb64e99f652f5c4f0cc2
+oid sha256:1a430cf9de43e68c4ba2a25448e0c7e7a89ff1736d38d71ff2492cddacc00b1d
 size 2098489

sac-seals-Walker2d-v0/data

@@ -4,17 +4,17 @@
         ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMCVNBQ1BvbGljeZSTlC4=",
         "__module__": "stable_baselines3.sac.policies",
         "__doc__": "\n    Policy class (with both actor and critic) for SAC.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
-        "__init__": "<function SACPolicy.__init__ at 0x7fc91ed020d0>",
-        "_build": "<function SACPolicy._build at 0x7fc91ed02160>",
-        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7fc91ed021f0>",
-        "reset_noise": "<function SACPolicy.reset_noise at 0x7fc91ed02280>",
-        "make_actor": "<function SACPolicy.make_actor at 0x7fc91ed02310>",
-        "make_critic": "<function SACPolicy.make_critic at 0x7fc91ed023a0>",
-        "forward": "<function SACPolicy.forward at 0x7fc91ed02430>",
-        "_predict": "<function SACPolicy._predict at 0x7fc91ed024c0>",
-        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7fc91ed02550>",
+        "__init__": "<function SACPolicy.__init__ at 0x7ff1e3c87ee0>",
+        "_build": "<function SACPolicy._build at 0x7ff1e3c87f70>",
+        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7ff1e3c10040>",
+        "reset_noise": "<function SACPolicy.reset_noise at 0x7ff1e3c100d0>",
+        "make_actor": "<function SACPolicy.make_actor at 0x7ff1e3c10160>",
+        "make_critic": "<function SACPolicy.make_critic at 0x7ff1e3c101f0>",
+        "forward": "<function SACPolicy.forward at 0x7ff1e3c10280>",
+        "_predict": "<function SACPolicy._predict at 0x7ff1e3c10310>",
+        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7ff1e3c103a0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fc91ecfa900>"
+        "_abc_impl": "<_abc_data object at 0x7ff1e3c86cc0>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -40,7 +40,7 @@
     },
     "action_space": {
         ":type:": "<class 'gym.spaces.box.Box'>",
-        ":serialized:": "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",
+        ":serialized:": "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",
         "dtype": "float32",
         "_shape": [
             6
@@ -55,17 +55,17 @@
     "num_timesteps": 1000000,
     "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
-    "seed": 0,
+    "seed": 9,
     "action_noise": null,
-    "start_time": 1658867341.4222457,
+    "start_time": 1672507717010268280,
     "learning_rate": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
-    "tensorboard_log": "runs/seals/Walker2d-v0__sac__9__1658867336/seals-Walker2d-v0",
+    "tensorboard_log": "runs/seals/Walker2d-v0__sac__5__1672507712/seals-Walker2d-v0",
     "lr_schedule": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_obs": null,
     "_last_episode_starts": {
@@ -74,7 +74,7 @@
     },
     "_last_original_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVBQEAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaQAAAAAAAAAGqDd90TvkJAZpNNisRh8D9Y6zSr4uLtv+ydyoLb9f6/KSTfivXKkb+QmHue3BDnv3YiEguUnnO/h/lNChsXA8Akc1zZzizqP0OhVSIAARdAXMGsJM9x7j/VepoCGRoWQFQL1ROz8BpAtMbeFhSFFcAAAAAAAAAkQDXEYbnQkhJAgA4whY+tDEAbrivXzDqmv5SMBW51bXB5lIwFZHR5cGWUk5SMAmY4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsShpSMAUOUdJRSlC4="
+        ":serialized:": "gAWVBQEAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaQAAAAAAAAAIvFGgl8eDpAO61oaHjV2z9YjVEyzPDtP1UOOyBwgwTAqnNbCbj+BcAMb9X5g37sP/o0SdjQDvG/jFXJcos2vD9eq2djgyzxvx5u28VWnQBA+R/GdA/SDEAAAAAAAAAkwJkzz8CDWBTAAAAAAAAAJMAAAAAAAAAkwAAAAAAAACTAQHYuWltXgj9SMKGnPyAhQJSMBW51bXB5lIwFZHR5cGWUk5SMAmY4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsShpSMAUOUdJRSlC4="
     },
     "_episode_num": 1000,
     "use_sde": false,
@@ -82,7 +82,7 @@
     "_current_progress_remaining": 0.0,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -100,13 +100,13 @@
         ":type:": "<class 'abc.ABCMeta'>",
         ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==",
         "__module__": "stable_baselines3.common.buffers",
-        "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device:\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
-        "__init__": "<function ReplayBuffer.__init__ at 0x7fc91ed4f280>",
-        "add": "<function ReplayBuffer.add at 0x7fc91ed4f310>",
-        "sample": "<function ReplayBuffer.sample at 0x7fc91ed4f3a0>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x7fc91ed4f430>",
+        "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
+        "__init__": "<function ReplayBuffer.__init__ at 0x7ff1e3c5c0d0>",
+        "add": "<function ReplayBuffer.add at 0x7ff1e3c5c160>",
+        "sample": "<function ReplayBuffer.sample at 0x7ff1e3c5c1f0>",
+        "_get_samples": "<function ReplayBuffer._get_samples at 0x7ff1e3c5c280>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fc91edd4480>"
+        "_abc_impl": "<_abc_data object at 0x7ff1e3cdd300>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {
@@ -116,5 +116,7 @@
     "use_sde_at_warmup": false,
     "target_entropy": -6.0,
     "ent_coef": "auto",
-    "target_update_interval": 1
+    "target_update_interval": 1,
+    "batch_norm_stats": [],
+    "batch_norm_stats_target": []
 }

sac-seals-Walker2d-v0/ent_coef_optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:68f1de550e86e01b6bb196030381f7d8f2a07e4528bff5ded6124a75ad19407b
-size 1443
+oid sha256:b514180cdb79442072ef81cdef6d2e1f5ec3a856d35b7f0df418e70f75ff997e
+size 1507

sac-seals-Walker2d-v0/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0728488ceeaf8e8a938a6b8a09633b86c78ad8d2ca8393bece3f07513f6c172a
+oid sha256:235465ba14b0776771df7bf881136a26d6906703aa8829e262aaaa44b9674fa4
 size 2625861

sac-seals-Walker2d-v0/pytorch_variables.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0454ce24fe9e06e273157d4b0eef01ca0d87fde278d816d6f962b224f6c84262
+oid sha256:87a3ecd2725bf728bd2ee5403c415377146315f773f5bf623e0180137fdba22b
 size 747

sac-seals-Walker2d-v0/system_info.txt

@@ -1,6 +1,6 @@
-OS: Linux-5.4.0-122-generic-x86_64-with-glibc2.29 #138-Ubuntu SMP Wed Jun 22 15:00:31 UTC 2022
+OS: Linux-5.4.0-125-generic-x86_64-with-glibc2.29 #141-Ubuntu SMP Wed Aug 10 13:42:03 UTC 2022
 Python: 3.8.10
-Stable-Baselines3: 1.6.0
+Stable-Baselines3: 1.6.2
 PyTorch: 1.11.0+cu102
 GPU Enabled: False
 Numpy: 1.22.3

train_eval_metrics.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:32a770dcf146bfd5619996c4df4100ed7f9044ea3d78f4bbc627f664fce45300
-size 34693
+oid sha256:dcabe724964590e4adfb5c96889aba152036ebe86b434e0a6ca315f54aaa9079
+size 34786