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: 2127.68 +/- 249.97
+      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:aabe0d29291539e9cacf8679936700d02d33731918b48430d8df08e4eb3a6500
+size 129259

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 0x7f1739675e50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1739675ee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1739675f70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f173967a040>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f173967a0d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f173967a160>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f173967a1f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f173967a280>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f173967a310>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f173967a3a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f173967a430>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f173967a4c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f17396743c0>"
+    },
+    "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": 1677142079878874679,
+    "learning_rate": 0.0007,
+    "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": 0.98,
+    "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:ab09fbaa3710dcec662a3b94fdc8783edeb23219ccbf9dda66506aa01a2697b3
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23b9a0fab70b356493c4ec00b83bdd9a63a48f0672a09a938b73179595885ab8
+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: 1.22.4
+- 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 0x7f1739675e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1739675ee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1739675f70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f173967a040>", "_build": "<function ActorCriticPolicy._build at 0x7f173967a0d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f173967a160>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f173967a1f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f173967a280>", "_predict": "<function ActorCriticPolicy._predict at 0x7f173967a310>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f173967a3a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f173967a430>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f173967a4c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f17396743c0>"}, "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": 1677142079878874679, "learning_rate": 0.0007, "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:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAAAAADTiYU2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAaLYivQAAAAADRwHAAAAAAIaVrbwAAAAAWgbrPwAAAAD5OZg9AAAAAP+l9T8AAAAAgbilvQAAAABSZuC/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAXL9WNgAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgDGBzz0AAAAAFirrvwAAAADzGoA9AAAAAGnr2D8AAAAAVQOcPQAAAAA8Idw/AAAAAGzqz70AAAAAZKLevwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABRIpDYAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIAfW7q9AAAAACtU6L8AAAAAs14JPgAAAAAC6OQ/AAAAAO1xtL0AAAAANAfZPwAAAAB/jJC9AAAAAO+T9b8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAYsCA2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAYDQ1vQAAAADDcd+/AAAAAF/FiT0AAAAAdTH/PwAAAADfItk9AAAAAHt29T8AAAAA7ozgPQAAAADJut2/AAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"}, "_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": 0.98, "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": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f0f6d437a3f290d3671e4af13a412288b38ae5d93e553385ff4d803d146f6ac
+size 1073296

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2127.6789023055, "std_reward": 249.96565622815916, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-23T09:52:53.645916"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebc38d0c6af8c0216211180803ba8516d607696045cff04bac9c2bb6defb4a5a
+size 2136