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PPO-CartPole-v1.zip

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PPO-CartPole-v1/_stable_baselines3_version

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PPO-CartPole-v1/data

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PPO-CartPole-v1/policy.optimizer.pth

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PPO-CartPole-v1/policy.pth

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PPO-CartPole-v1/pytorch_variables.pth

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PPO-CartPole-v1/system_info.txt

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+- OS: Linux-6.5.0-27-generic-x86_64-with-glibc2.35 # 28~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Mar 15 10:51:06 UTC 2
+- Python: 3.9.18
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.2.1+cpu
+- GPU Enabled: False
+- Numpy: 1.26.4
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.26.2

README.md

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+---
+library_name: stable-baselines3
+tags:
+- CartPole-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CartPole-v1
+      type: CartPole-v1
+    metrics:
+    - type: mean_reward
+      value: 483.70 +/- 35.69
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **CartPole-v1**
+This is a trained model of a **PPO** agent playing **CartPole-v1**
+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 0x7772654e9f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7772654ea040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7772654ea0d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7772654ea160>", "_build": "<function ActorCriticPolicy._build at 0x7772654ea1f0>", "forward": "<function ActorCriticPolicy.forward at 0x7772654ea280>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7772654ea310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7772654ea3a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7772654ea430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7772654ea4c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7772654ea550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7772654ea5e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7772654ed440>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 4096, "_total_timesteps": 3000, 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results.json

@@ -0,0 +1 @@
+{"mean_reward": 483.7, "std_reward": 35.69327667782828, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-16T11:39:00.440904"}