Upload PPO LunarLander-v2 trained agent

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 233.08 +/- 26.89
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+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 0x73ffe8485800>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x73ffe84858a0>", "reset_noise": 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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/_stable_baselines3_version

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ppo-LunarLander-v2/data

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ppo-LunarLander-v2/policy.optimizer.pth

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ppo-LunarLander-v2/system_info.txt

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+- OS: Linux-6.8.0-1024-aws-x86_64-with-glibc2.39 # 26-Ubuntu SMP Tue Feb 18 17:22:37 UTC 2025
+- Python: 3.12.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.8.0+cu128
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.2
+- Gymnasium: 0.28.1

results.json

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+{"mean_reward": 233.08172072156304, "std_reward": 26.88859083492, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2026-06-29T17:51:47.335780"}