Initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 1457.85 +/- 32.18
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-AntBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9a9f8031b947bf1a856497115fe05930035fa480270e365fdf1f5a87b7d84fb
+size 129266

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,106 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7ff7848171f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff784817280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff784817310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff7848173a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff784817430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff7848174c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff784817550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff7848175e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff784817670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff784817700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff784817790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff784817820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ff784816c00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            28
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/AACAvwAAgL8AAIC/AACAv5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAQEBAQEBAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAEBAQEBAQEBlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678702936358288511,
+    "learning_rate": 9.6e-05,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJK2sfozN2WMAWyUTegDjAF0lEdArxuk+PikwnV9lChoBkdAkiYizHCGe2gHTegDaAhHQK8do+LWI451fZQoaAZHQJRucTK1XvJoB03oA2gIR0CvH1jKxLTQdX2UKGgGR0CTqnQumJm/aAdN6ANoCEdAryIzdnCfpXV9lChoBkdAlOT9iQT24GgHTegDaAhHQK8rSFFDv3J1fZQoaAZHQJHtjCwbEP1oB03oA2gIR0CvLJX40uUVdX2UKGgGR0CT/MPUKArhaAdN6ANoCEdAry20kB0ZFXV9lChoBkdAhuMB6rvLHWgHTegDaAhHQK8vqNpdrwh1fZQoaAZHQJIJPegte2NoB03oA2gIR0CvOd04JeE7dX2UKGgGR0CTjhNlAeJYaAdN6ANoCEdArzvZDZ13dXV9lChoBkdAkZTRLf1pTWgHTegDaAhHQK89kaWHDaZ1fZQoaAZHQI2YCmKqGURoB03oA2gIR0CvQIUNrj5sdX2UKGgGR0CQR5izcAR1aAdN6ANoCEdAr0og9mpVCHV9lChoBkdAku8ILXtjTmgHTegDaAhHQK9La3eenQ91fZQoaAZHQJQ4qKfnOjZoB03oA2gIR0CvTIbqptJndX2UKGgGR0CTQMQpF1B/aAdN6ANoCEdAr055nOB193V9lChoBkdAjzcHBk7OmmgHTegDaAhHQK9YYS9ugpV1fZQoaAZHQI/A2yHEdeZoB03oA2gIR0CvWlDTz/ZNdX2UKGgGR0CREzo11nuiaAdN6ANoCEdAr1v59AooeHV9lChoBkdAks+hfShJy2gHTegDaAhHQK9e35xiobZ1fZQoaAZHQJNmOGEf1YhoB03oA2gIR0CvaQVdHDrJdX2UKGgGR0CT9JKs+3YuaAdN6ANoCEdAr2pS//NqxnV9lChoBkdAkl5Xfdhy82gHTegDaAhHQK9rcjbi6xx1fZQoaAZHQJJfEdlum79oB03oA2gIR0CvbVsqBmPHdX2UKGgGR0CRRVFMqSX/aAdN6ANoCEdAr3acKeCkGnV9lChoBkdAlD4MPSUkfWgHTegDaAhHQK94hksBhhJ1fZQoaAZHQJVH1Db8FZBoB03oA2gIR0CvehSofjjrdX2UKGgGR0CR7M91loUSaAdN6ANoCEdAr3zyZlWfb3V9lChoBkdAk9y8MI/qxGgHTegDaAhHQK+H844p+c91fZQoaAZHQJOeYW+GoJloB03oA2gIR0CviT1eBxxUdX2UKGgGR0CTZfpIMBp6aAdN6ANoCEdAr4pcYIjW1HV9lChoBkdAkHdCswL3K2gHTegDaAhHQK+MQxY7q6h1fZQoaAZHQJJrKswL3K1oB03oA2gIR0CvlU6K1og3dX2UKGgGR0CTBNRF7UobaAdN6ANoCEdAr5bY1k1/D3V9lChoBkdAk4G+8Gs3hmgHTegDaAhHQK+YcrDqGDd1fZQoaAZHQJQCH09QoCxoB03oA2gIR0Cvmynv2GqQdX2UKGgGR0CTzUPhybQUaAdN6ANoCEdAr6atwm3OOnV9lChoBkdAjwIwyqMm4WgHTegDaAhHQK+n/mBe5Wl1fZQoaAZHQJS+sUbkwN9oB03oA2gIR0CvqRjH4oJBdX2UKGgGR0CSDddCVrylaAdN6ANoCEdAr6r7ypaRp3V9lChoBkdAk2tchs67umgHTegDaAhHQK+0FXXiBGx1fZQoaAZHQItfipJf6XVoB03oA2gIR0CvtWUDlo12dX2UKGgGR0CUJH4lQdjoaAdN6ANoCEdAr7ao8jiXIHV9lChoBkdAkfHTh1klNWgHTegDaAhHQK+5WntOVPh1fZQoaAZHQJKmBKTSssBoB03oA2gIR0CvxY61LJ0XdX2UKGgGR0CTERpYLb5/aAdN6ANoCEdAr8bZhBqsVHV9lChoBkdAkyHkU47zTWgHTegDaAhHQK/H/mL9/Bp1fZQoaAZHQJLYMlt0mt1oB03oA2gIR0CvyexO1v2odX2UKGgGR0CTRlewLVnVaAdN6ANoCEdAr9L6Gxlg+nV9lChoBkdAjpae6Zpi7WgHTegDaAhHQK/UTjFyaNN1fZQoaAZHQJJ4PMt9QXRoB03oA2gIR0Cv1Wx1PnB+dX2UKGgGR0CSTT7vG6wuaAdN6ANoCEdAr9fP974SH3V9lChoBkdAk5+rHU+cIGgHTegDaAhHQK/kZDbah6B1fZQoaAZHQJDLx2dNFjNoB03oA2gIR0Cv5a/vF3pwdX2UKGgGR0CPT1hXr+o+aAdN6ANoCEdAr+bN2gWadHV9lChoBkdAkWAX2dupCWgHTegDaAhHQK/otUJfICF1fZQoaAZHQJQERxWDHwRoB03oA2gIR0Cv8c1/2Cd0dX2UKGgGR0CUyFoqCpWFaAdN6ANoCEdAr/MatJWeYnV9lChoBkdAkFZiFj/dZmgHTegDaAhHQK/0OeEqUeN1fZQoaAZHQJI01B+nZTRoB03oA2gIR0Cv9iHWBjFydX2UKGgGR0CSmi2ECeVcaAdN6ANoCEdAsAGrpSrHVHV9lChoBkdAj/TZPuXu3WgHTegDaAhHQLACUd43WFx1fZQoaAZHQJIc4wL3K0VoB03oA2gIR0CwAuBa9sabdX2UKGgGR0CStck+HJtBaAdN6ANoCEdAsAPTfbblBHV9lChoBkdAke1HDaXa8GgHTegDaAhHQLAIWjurp7l1fZQoaAZHQJJlRiUgSvloB03oA2gIR0CwCQLi++M7dX2UKGgGR0CR4lVM23rlaAdN6ANoCEdAsAmR0wJw9HV9lChoBkdAkw6iRGMGYGgHTegDaAhHQLAKhCI1tO51fZQoaAZHQJKgA0YTCchoB03oA2gIR0CwEO00BOpLdX2UKGgGR0CTkh1P3ztkaAdN6ANoCEdAsBGvU1AJLXV9lChoBkdAksKszqKP4mgHTegDaAhHQLASPizsyBV1fZQoaAZHQI99W/N7jT9oB03oA2gIR0CwEy6M72csdX2UKGgGR0CRSFIp6QeWaAdN6ANoCEdAsBe0IsyzonV9lChoBkdAlO9g+QlrumgHTegDaAhHQLAYXJhvze51fZQoaAZHQJQZfpC8e0ZoB03oA2gIR0CwGOpbt7a7dX2UKGgGR0CR8atj0+TvaAdN6ANoCEdAsBnf/JeVs3V9lChoBkdAlJkCYCyQgmgHTegDaAhHQLAf8HNX5nF1fZQoaAZHQJHyQGorFwVoB03oA2gIR0CwIPK7mMfjdX2UKGgGR0CSpGy7wrlOaAdN6ANoCEdAsCGbWSU1RHV9lChoBkdAlAwjgAIY32gHTegDaAhHQLAikL26ClJ1fZQoaAZHQJIn6u3c581oB03oA2gIR0CwJxzesPrfdX2UKGgGR0CLc6DRtxdZaAdN6ANoCEdAsCfFnXd0rHV9lChoBkdAkoitQGfPHGgHTegDaAhHQLAoU4x1xKh1fZQoaAZHQJK4W3ocJdBoB03oA2gIR0CwKUlkUbkwdX2UKGgGR0CRUUUc4o7WaAdN6ANoCEdAsC8HMotth3V9lChoBkdAkmtfrKNhmWgHTegDaAhHQLAwCkkrwvx1fZQoaAZHQJGzv0nPVutoB03oA2gIR0CwMOJAMUh3dX2UKGgGR0CVobbhWHUMaAdN6ANoCEdAsDHyRHPNV3V9lChoBkdAkxD7oSteU2gHTegDaAhHQLA2gPSDyvt1fZQoaAZHQJAsaXw9aEBoB03oA2gIR0CwNyq5f+judX2UKGgGR0CQy3ExqO94aAdN6ANoCEdAsDe6F10T13V9lChoBkdAkSq8wpON52gHTegDaAhHQLA4rk9ECvJ1fZQoaAZHQJSjacqe9SNoB03oA2gIR0CwPiWpAD7qdX2UKGgGR0CT1BKFqSHNaAdN6ANoCEdAsD8m4I8hcXV9lChoBkdAkUk7FfiPyWgHTegDaAhHQLBAAkiD/VB1fZQoaAZHQJOvjujRD1JoB03oA2gIR0CwQWSW3Sa3dX2UKGgGR0CUfepPAO8TaAdN6ANoCEdAsEXoQqZtvXV9lChoBkdAlEb68UVSGmgHTegDaAhHQLBGkM98qnZ1fZQoaAZHQJJkBzeXRgJoB03oA2gIR0CwRyDklu3udX2UKGgGR0CTO9n8sMAnaAdN6ANoCEdAsEgWtV7x/nVlLg=="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 31250,
+    "n_steps": 16,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09a805c36fe4bd728e388f6ba4fbec4293e67eb3803e9e17b0fe5039f160e72f
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84796d2d61e065c07da7febec3ee95a60b41a5c2a0ebbe603a9034c9c4980507
+size 56958

a2c-AntBulletEnv-v0/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-AntBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7ff7848171f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff784817280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff784817310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff7848173a0>", "_build": "<function ActorCriticPolicy._build at 0x7ff784817430>", "forward": "<function ActorCriticPolicy.forward at 0x7ff7848174c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff784817550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff7848175e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff784817670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff784817700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff784817790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff784817820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff784816c00>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVZwIAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLHIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWcAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lGgKSxyFlIwBQ5R0lFKUjARoaWdolGgSKJZwAAAAAAAAAAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH+UaApLHIWUaBV0lFKUjA1ib3VuZGVkX2JlbG93lGgSKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLHIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaCFLHIWUaBV0lFKUjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678702936358288511, "learning_rate": 9.6e-05, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 31250, "n_steps": 16, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c41ac7a09019c3ab5caf2a6eb8026eeab89f6c94cc8547c151ccba1e3532dbd2
+size 1099521

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1457.8508475503884, "std_reward": 32.18454267807113, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-13T11:37:07.432985"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dee268e10b6716a3791ca3a78e89c4860eee854f3d1a5a6bf1682b2d3667f8fe
+size 2136