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: 1662.86 +/- 232.53
+      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 sha256:161b7aba2e79f047e7258e24e8d12114f1f5b19f22c3c15210e41f940c777277
+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 0x7f7380aa7c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7380aa7ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7380aa7d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7380aa7dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7380aa7e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f7380aa7ee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7380aa7f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7380aab040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f7380aab0d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7380aab160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7380aab1f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7380aab280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f7380aa1c00>"
+    },
+    "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": 1674481587086818105,
+    "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:d98a017edf056bfad6e506fe1fb2c88b0f1d692be7e0c9f43f00d49c04d21f56
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:772a487d21d57802059c0854a37c7e6ef7216d0d57fbb537238e1215e90d17fd
+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 0x7f7380aa7c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7380aa7ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7380aa7d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7380aa7dc0>", "_build": "<function ActorCriticPolicy._build at 0x7f7380aa7e50>", "forward": "<function ActorCriticPolicy.forward at 0x7f7380aa7ee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7380aa7f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7380aab040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7380aab0d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7380aab160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7380aab1f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7380aab280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7380aa1c00>"}, "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": 1674481587086818105, "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:f72c520c7c838fc6debd6032561b35104b2419bcd228506522c0ea5ae6e99d83
+size 1050464

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1662.85500057732, "std_reward": 232.53174404450894, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-23T14:41:51.579484"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:986f422d6095d21243ccb689447dbeb3e911512b6ef8da6ed55de8ddba4b5525
+size 2136