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: 626.96 +/- 171.22
+      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:bb42270ecf8d710eff75575e3a3c4261a0aa8c19e244154af96660e48c9f0f07
+size 129248

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 0x7f4af45270a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4af4527130>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4af45271c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4af4527250>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4af45272e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4af4527370>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4af4527400>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4af4527490>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4af4527520>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4af45275b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4af4527640>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4af45276d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f4af4514540>"
+    },
+    "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": 1682656809289141397,
+    "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 sha256:4775f5fc83e5c87f31bc0aac448a6f2c8cb0e7cafcdf25a181c67e09252a1bc4
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad050f072bd04e25d3bb5735e257eddbca7b40090ce72e0cc856d3a805224b1d
+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: 3.10.11
+- 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 0x7f4af45270a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4af4527130>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4af45271c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4af4527250>", "_build": "<function ActorCriticPolicy._build at 0x7f4af45272e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f4af4527370>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4af4527400>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4af4527490>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4af4527520>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4af45275b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4af4527640>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4af45276d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f4af4514540>"}, "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": 1682656809289141397, "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": "3.10.11", "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 sha256:198253af7454c2d580c95f9cd34d9d7c83b1db38e735bb580e706a6202f51a2a
+size 1091208

results.json

@@ -0,0 +1 @@
+{"mean_reward": 626.9566892117916, "std_reward": 171.21516305343934, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-28T08:53:11.122911"}