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: 1511.88 +/- 232.26
+      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:af4823c8c0a3923b9de0c99ac9274e601940f81aa4e656bb53f9b22a2c90bb0d
+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 0x7f925aa00ca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f925aa00d30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f925aa00dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f925aa00e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f925aa00ee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f925aa00f70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f925aa04040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f925aa040d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f925aa04160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f925aa041f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f925aa04280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f925aa04310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f925a9e3c00>"
+    },
+    "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": 1682085163326494415,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/T3UQTVUdaYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_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 sha256:2bb10e01b0f4b336b8dc44235e9f98ee6739892bdfc142d913f2e91e1790aabe
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4386ff246e265d70cd52e5a2be1ad2c61a4743b9296072cc89c07d9c8f059acc
+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 0x7f925aa00ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f925aa00d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f925aa00dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f925aa00e50>", "_build": "<function ActorCriticPolicy._build at 0x7f925aa00ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f925aa00f70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f925aa04040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f925aa040d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f925aa04160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f925aa041f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f925aa04280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f925aa04310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f925a9e3c00>"}, "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": 1682085163326494415, "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 sha256:66c345ded9cd81097a2429c1f57a79a900aaac1c30c5bd73a121f295a42b93c1
+size 1104613

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1511.884272568964, "std_reward": 232.26126253438366, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-21T14:54:42.931061"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80367bac1617c310d717aa6de3da051257aeccc75d75bad8c42ce86530237d91
+size 2170