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: 1781.47 +/- 144.71
+      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 sha256:125f40d2e127d9ba754e79677c790c0fce1a6f5dc5726c083492c9fa13772b55
+size 129229

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 0x7f8d8fc04790>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8d8fc04820>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8d8fc048b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8d8fc04940>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f8d8fc049d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f8d8fc04a60>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8d8fc04af0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8d8fc04b80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f8d8fc04c10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8d8fc04ca0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8d8fc04d30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8d8fc04dc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f8d8fc05b80>"
+    },
+    "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": 3000000,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681666822493871529,
+    "learning_rate": 0.001,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/UGJN0vGp/IWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_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": 93750,
+    "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:c9d492a2783219994935c8085d41de375302768acfaefd366de624493548666f
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ebc5d4ace866d6c7d0a1f53e192dda2a6e1362b88d67ac33083c9a25b420cb8
+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 0x7f8d8fc04790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8d8fc04820>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8d8fc048b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8d8fc04940>", "_build": "<function ActorCriticPolicy._build at 0x7f8d8fc049d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f8d8fc04a60>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8d8fc04af0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8d8fc04b80>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8d8fc04c10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8d8fc04ca0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8d8fc04d30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8d8fc04dc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f8d8fc05b80>"}, "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": 3000000, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681666822493871529, "learning_rate": 0.001, "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": 93750, "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 sha256:9a4dcf37c5696263a705b64f3af35e164eaf97d0fe5e918b934d97d15cdd3491
+size 1108986

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1781.4749269756721, "std_reward": 144.70854067676643, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-16T19:04:17.865569"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be81e0f191998ac58fbe9d7a418c2bd48b074c05773a7ce422eacd00b25e8642
+size 2170