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: 1653.72 +/- 135.31
+      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 sha256:4a41504f4e262dde1a8217472669ac4dcb79af0ed969f4f617505daf009d1796
+size 129260

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 0x7fdc428c5ca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdc428c5d30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdc428c5dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdc428c5e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fdc428c5ee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fdc428c5f70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdc428c9040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdc428c90d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fdc428c9160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdc428c91f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdc428c9280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdc428c9310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fdc428bee40>"
+    },
+    "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": 3000000,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1675747860404993894,
+    "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": 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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4de923b835ed59909f283c58ac2bb4d34c0afe6be1e73abdabf0b41434f6627
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d0c8430df1fe56ba3d0c77210c0ce1af568dc81c77df7307a539ce70b707994
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- 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 0x7fdc428c5ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdc428c5d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdc428c5dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdc428c5e50>", "_build": "<function ActorCriticPolicy._build at 0x7fdc428c5ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7fdc428c5f70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdc428c9040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdc428c90d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fdc428c9160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdc428c91f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdc428c9280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdc428c9310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fdc428bee40>"}, "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": 3000000, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675747860404993894, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+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, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:86509a313be3dfe6cf1a18e38542af54792dee6d3c5a8df56f5d8abc80773702
+size 1231838

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1653.724389279756, "std_reward": 135.3063726521441, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-07T07:38:15.919475"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18bb18c4414f5fa913a4481884ca3c07f009fa53349df953ffd2bd8fd4a0ccc8
+size 2136