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: 1556.86 +/- 35.82
+      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 sha256:8e15d03ac59f72ac786f890c7d90f356f543b1f5c71623a29af9453f19302199
+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 0x7f41bed10e50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f41bed10ee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f41bed10f70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f41bed12040>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f41bed120d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f41bed12160>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f41bed121f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f41bed12280>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f41bed12310>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f41bed123a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f41bed12430>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f41bed124c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f41bed0de00>"
+    },
+    "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": 1678884827485877331,
+    "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 sha256:6bfa74884488effb6631baeb10bda1379440f80fd068eebab8a167b41a535ea5
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:323d8a3c3197ac6989bbe760b9b9b0b9cb068657d6cf6a74db5ce324698f3fb7
+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 0x7f41bed10e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f41bed10ee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f41bed10f70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f41bed12040>", "_build": "<function ActorCriticPolicy._build at 0x7f41bed120d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f41bed12160>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f41bed121f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f41bed12280>", "_predict": "<function ActorCriticPolicy._predict at 0x7f41bed12310>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f41bed123a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f41bed12430>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f41bed124c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f41bed0de00>"}, "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": 1678884827485877331, "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 sha256:6d39973b22cc9cb5121657ebbb001606c95abe98daf59c412bf89d1931f6ca9a
+size 1062184

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1556.860543397686, "std_reward": 35.822582647515645, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-15T13:51:33.574517"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:611f7472e08d4556d95a0bd3b4a04923e1bb6a56fb7642a64ce1125a5c763fde
+size 2136