Initial commit

.gitattributes

@@ -33,3 +33,4 @@ 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: 1725.95 +/- 117.76
+      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 sha256:5fde8fee979251816872de227ac6d037d5de4563c693d6472877a5bdfc30379d
+size 128992

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,107 @@
+{
+    "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 0x794b805fba30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x794b805fbac0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x794b805fbb50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x794b805fbbe0>",
+        "_build": "<function ActorCriticPolicy._build at 0x794b805fbc70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x794b805fbd00>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x794b805fbd90>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x794b805fbe20>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x794b805fbeb0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x794b805fbf40>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x794b80608040>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x794b806080d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x794b805f3680>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1691318211672016237,
+    "learning_rate": 0.00096,
+    "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,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 62500,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false,
+    "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:": "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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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d39c5849d18ace99ab3859bebf470fd36da40d90c22ad083b28cbdbbc4c37628
+size 56062

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2831127733db18bff9c35727d7c2780c68db0b4d260ad6d9bddf5c5a7995f8f8
+size 56766

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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: False
+- 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 0x794b805fba30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x794b805fbac0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x794b805fbb50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x794b805fbbe0>", "_build": "<function ActorCriticPolicy._build at 0x794b805fbc70>", "forward": "<function ActorCriticPolicy.forward at 0x794b805fbd00>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x794b805fbd90>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x794b805fbe20>", "_predict": "<function ActorCriticPolicy._predict at 0x794b805fbeb0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x794b805fbf40>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x794b80608040>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x794b806080d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x794b805f3680>"}, "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}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1691318211672016237, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAEsbzj407IW/BHl5v8ddCj+l1Ve/SS21PzSBuL9KMyC+s182v44UOz8ho5A+6+OxPqVvez/Gl0Y9GaoGP4h3vTx2B4u/A7Wfv+0zp7+QKTk/bauSv1rBGT+sU5U/IIUqv/T9KT+Ro5c+vwcvPwG7Or/s6P4+SRNivz+oGr/pCU4/IOWAvsqFIMCppLs+QrvjvtJgxT4MasK/PMZ5PoM71r82pWs/Sf0vPyVfOb5EP6A+UyMDv58yrj807n8/CDbevRlu8b5rFPm+7NyfPgTv1T8Tw8C/kaOXPp02u78Buzq/BW1DPeRHcb/RYTq/6jEaP9UmOD+/qJe/SwYLP1XSPb5pTQe/VYnBvz9rST4JdNi/N8LhP/jqkj6bGpK+/KSpP4EBjT59tPE/4hB8PzhLgL/dZqm+e2KvvGMskT+DeC4/E8PAv5Gjlz6dNru/Abs6vzx4Yz9fO1+/owQVv6Et8z8GU0u/L5/8Pvu1L79Ogk2/s3WyPu0Lpj8WfqQ/X+7RPuTR/D4Ab1c+2RIIPxEGBb3DHsO/v0ABv9jMYj6KrtY/LWxfvrFIwz5/aCs+oVNzv/T9KT+Ro5c+vwcvPwG7Or+UjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"}, "_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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "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:": "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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, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce2bf12286367c96fbd9765efc253121c8d5ded2c21bcbf34ea22fc7fac7d74e
+size 1244986

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1725.9469075333104, "std_reward": 117.75602927908646, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-06T11:18:37.745571"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebf4554d850ea026d70466641ea9cafb0ea397808ef4ad6d8f4978cf6decdc17
+size 2176