Initial commit

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: 1379.53 +/- 315.94
+      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:a56a7fef8417679bac1edf2d6a67475630b56f271bd06bb8ee2cd81a2bd242dd
+size 129256

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 0x7f2e4000da60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2e4000daf0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2e4000db80>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2e4000dc10>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f2e4000dca0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f2e4000dd30>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2e4000ddc0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2e4000de50>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f2e4000dee0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2e4000df70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2e40011040>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2e400110d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f2e4000b210>"
+    },
+    "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": 1675345108155600076,
+    "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:c6c890be2784c57e9d908d9fc837c0c61d3524b9ef239045252340e7ff0bc2da
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0bd30ec80ede4a6f6bb0e634d31e5b2ea23a002ebdb7bfd4bb94b3e80faad55
+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 0x7f2e4000da60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2e4000daf0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2e4000db80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2e4000dc10>", "_build": "<function ActorCriticPolicy._build at 0x7f2e4000dca0>", "forward": "<function ActorCriticPolicy.forward at 0x7f2e4000dd30>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2e4000ddc0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2e4000de50>", "_predict": "<function ActorCriticPolicy._predict at 0x7f2e4000dee0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2e4000df70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2e40011040>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2e400110d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f2e4000b210>"}, "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": 1675345108155600076, "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-glibc2.29 # 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"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1379.5286674123258, "std_reward": 315.94084579944655, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-02T14:34:31.611484"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee26d9e96702f177636182ecb4b3ad57f55201d80dc3b6d48ef4941b7cc85f31
+size 2136