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: 1733.04 +/- 314.67
+      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:fb6dc981d8bee6de109ee3407d494c86bf4a1ecf3ebb4b9f0b21e075490fa701
+size 129231

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 0x7fe726a4b550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe726a4b5e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe726a4b670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe726a4b700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fe726a4b790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fe726a4b820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe726a4b8b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe726a4b940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fe726a4b9d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe726a4ba60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe726a4baf0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe726a4bb80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fe726a4d200>"
+    },
+    "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": 1682448219143219882,
+    "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 sha256:95c115321824a5dd82eb46b7adea868bd38e89fd2b01472d0dd41cc518e95d0c
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc9adc8d4604dbf7ba92c080794c54a0d45a987c4be6f56c35fd77829d8d2afa
+size 56894

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: 1.8.0
+- PyTorch: 2.0.0+cu118
+- 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 0x7fe726a4b550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe726a4b5e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe726a4b670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe726a4b700>", "_build": "<function ActorCriticPolicy._build at 0x7fe726a4b790>", "forward": "<function ActorCriticPolicy.forward at 0x7fe726a4b820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe726a4b8b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe726a4b940>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe726a4b9d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe726a4ba60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe726a4baf0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe726a4bb80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe726a4d200>"}, "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": 1682448219143219882, "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, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "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 sha256:102a5f456b1d0429eb20edc11820c8bb840f83d53ddc6c2bb99f528ce6752523
+size 1078543

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1733.0375909750728, "std_reward": 314.67325949620914, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-25T19:45:33.393466"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3f0f803e6cddccc9226978f2cadb17cbbc02fc571e0fb9fb65c772627411951
+size 2170