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: 1829.43 +/- 423.83
+      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:b8551cb98c766a65974279b8a70b57d7fcbb5d87d178382c1d11869831773b12
+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 0x7fe9a07ea9d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe9a07eaa60>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe9a07eaaf0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe9a07eab80>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fe9a07eac10>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fe9a07eaca0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe9a07ead30>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe9a07eadc0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fe9a07eae50>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe9a07eaee0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe9a07eaf70>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe9a07ed040>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fe9a07ebe00>"
+    },
+    "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": 1682528904496462980,
+    "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 sha256:2b157cb81177de350a011c0d3ca68b5001e31c7f3c0934856bb16060a103ec5b
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6b9cb1762026c9206d6dcbda1891f01d100262954d31ac773898c448a590801
+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 0x7fe9a07ea9d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe9a07eaa60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe9a07eaaf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe9a07eab80>", "_build": "<function ActorCriticPolicy._build at 0x7fe9a07eac10>", "forward": "<function ActorCriticPolicy.forward at 0x7fe9a07eaca0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe9a07ead30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe9a07eadc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe9a07eae50>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe9a07eaee0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe9a07eaf70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe9a07ed040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe9a07ebe00>"}, "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": 1682528904496462980, "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:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/AACAvwAAgL8AAIC/AACAv5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAQEBAQEBAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAEBAQEBAQEBlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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 sha256:9776cc197616dffa60f36bc217ca4b53d8ba61dba470af7c666a3b57dbad76e5
+size 1200224

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1829.4334415941498, "std_reward": 423.8307194515349, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-26T18:08:28.619064"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14072e8135cdab85c1d1ff2a87cf6437d64e9f4e9490f7c088d267f878f1a81d
+size 2170