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: 2266.52 +/- 41.96
+      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 sha256:08ecceaf9f9c6d8d2d4804c79eff970a1b25fa2c4d615bfcd8f4a744ab6b5934
+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 0x7f9916c5fb80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9916c5fc10>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9916c5fca0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9916c5fd30>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f9916c5fdc0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f9916c5fe50>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9916c5fee0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9916c5ff70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f9916c64040>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9916c640d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9916c64160>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9916c641f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f9916c5c750>"
+    },
+    "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": 1675163784005168982,
+    "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 sha256:9342dada9934e88d181e59c0f6bf2c115987badef18d17696b4adbb9e17ba3eb
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b6a9bcff09ea740a716335e6a70faf218f0acab20e9ea7eb3e39d8a4b443452
+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 0x7f9916c5fb80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9916c5fc10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9916c5fca0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9916c5fd30>", "_build": "<function ActorCriticPolicy._build at 0x7f9916c5fdc0>", "forward": "<function ActorCriticPolicy.forward at 0x7f9916c5fe50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9916c5fee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9916c5ff70>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9916c64040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9916c640d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9916c64160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9916c641f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9916c5c750>"}, "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": 1675163784005168982, "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"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ef3d9e3821a32c60e091221f81dd5a45731ca4e37c5b82e1e043989f874ecf1
+size 1069344

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2266.520287480601, "std_reward": 41.95523389789675, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-31T12:15:27.130831"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d938a3a57568776ec2a014bfef8aab9807288e65a35a1d689de4cc380a98d1b5
+size 2136