Uploaded PPO-BipedalWalker-v3

PPO-BipedalWalker-v3.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1041735f4273d3284e2239d0b42754d9607f49c7dec4c8c85666204ed204b0f3
+size 175610

PPO-BipedalWalker-v3/_stable_baselines3_version

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+2.1.0

PPO-BipedalWalker-v3/data

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+{
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+        "__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    ",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dcb48182830>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dcb481828c0>",
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+        "forward": "<function ActorCriticPolicy.forward at 0x7dcb481829e0>",
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+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dcb48182d40>",
+        "__abstractmethods__": "frozenset()",
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+    }
+}

PPO-BipedalWalker-v3/policy.optimizer.pth

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+size 105008

PPO-BipedalWalker-v3/policy.pth

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+size 51838

PPO-BipedalWalker-v3/pytorch_variables.pth

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+version https://git-lfs.github.com/spec/v1
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+size 431

PPO-BipedalWalker-v3/system_info.txt

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+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.25.2

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- BipedalWalker-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: BipedalWalker-v3
+      type: BipedalWalker-v3
+    metrics:
+    - type: mean_reward
+      value: 169.50 +/- 155.55
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **BipedalWalker-v3**
+This is a trained model of a **PPO** agent playing **BipedalWalker-v3**
+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
+
+...
+```

config.json

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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 0x7dcb48182710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dcb481827a0>", "reset_noise": 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results.json

@@ -0,0 +1 @@
+{"mean_reward": 169.49534529630108, "std_reward": 155.5542038384289, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-15T23:52:56.921247"}