Initial commit

.gitattributes

@@ -33,3 +33,4 @@ 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: 1402.29 +/- 91.75
+      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:e51d48ad5c43f066774270accb9b5e1a1838c83f0586ffdb602ba5c75e910f61
+size 129246

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,107 @@
+{
+    "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 0x7ebb8599e0e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ebb8599e170>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ebb8599e200>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ebb8599e290>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ebb8599e320>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ebb8599e3b0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ebb8599e440>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ebb8599e4d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ebb8599e560>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ebb8599e5f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ebb8599e680>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ebb8599e710>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ebb85996980>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690882817999034177,
+    "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,
+    "_stats_window_size": 100,
+    "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,
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89382ad1e122bdfec27e6dd9a49e50ee29f4ef79d88981314af7ab16a4eb6d53
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66e7ba2f63eeefa58591cd6583decde3435970f808b375875332cf6b82148697
+size 56894

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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- 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 0x7ebb8599e0e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ebb8599e170>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ebb8599e200>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ebb8599e290>", "_build": "<function ActorCriticPolicy._build at 0x7ebb8599e320>", "forward": "<function ActorCriticPolicy.forward at 0x7ebb8599e3b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ebb8599e440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ebb8599e4d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7ebb8599e560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ebb8599e5f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ebb8599e680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ebb8599e710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ebb85996980>"}, "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}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690882817999034177, "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, "_stats_window_size": 100, "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, "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, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "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 sha256:3bb4417287db6a6965d51d159688d861390526356897bef9b28fee14245943a9
+size 1023234

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1402.2853984104847, "std_reward": 91.7513407959235, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-01T10:41:06.089358"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:182ba60944ec4f742322c029eafe48bf28329ac2cf05c5024a9f370d1bc9abf5
+size 2176