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: 2121.26 +/- 116.83
+      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 sha256:5cf41556e4df7258e8a3f8b7d0b2b00fcf694645eb3e577d4375431c9390ce6f
+size 129265

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 0x7fa55a26b5e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa55a26b670>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa55a26b700>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa55a26b790>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa55a26b820>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa55a26b8b0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa55a26b940>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa55a26b9d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa55a26ba60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa55a26baf0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa55a26bb80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa55a26bc10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fa55a26ca80>"
+    },
+    "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:": "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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": 1679943850651775302,
+    "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,
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:177583d7d0989987ffcb2c7e67f68d3c409c0f9c17ae9073a067f798677440ba
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4334d0b5d2d17f11ec69883d61adcd6b64989ca0819f472c7f0819c70005a63f
+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 0x7fa55a26b5e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa55a26b670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa55a26b700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa55a26b790>", "_build": "<function ActorCriticPolicy._build at 0x7fa55a26b820>", "forward": "<function ActorCriticPolicy.forward at 0x7fa55a26b8b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa55a26b940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa55a26b9d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa55a26ba60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa55a26baf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa55a26bb80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa55a26bc10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa55a26ca80>"}, "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": 1679943850651775302, "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, "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, "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 sha256:7ef2102d79217d50bda3fd60cbaef447ab265d6f8214ef531b78ddc87b8ee7f4
+size 1051770

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2121.2633238857497, "std_reward": 116.83190236124354, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T20:05:31.329130"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c81660a85667a6ce0139ad1386a149ffefd6c62e5f3d0c33d4849918c7cbd79
+size 2136