Upload PPO LunarLander-v2 model

.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

@@ -1,57 +1,37 @@
 ---
+library_name: stable-baselines3
 tags:
 - LunarLander-v2
-- ppo
-- deep-rl-course
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
 model-index:
 - name: PPO
   results:
-  - metrics:
-    - type: mean_reward
-      value: 116.62 +/- 109.65
-      name: mean_reward
-    task:
+  - task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
       name: LunarLander-v2
       type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: -1070.34 +/- 1311.70
+      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) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
-
-The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
-
-## Usage (with SB3 RL Zoo)
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
-RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
-SB3: https://github.com/DLR-RM/stable-baselines3<br/>
-SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+## Usage (with Stable-baselines3)
+TODO: Add your code
 
-```
-# Download model and save it into the logs/ folder
-python -m rl_zoo3.load_from_hub --algo ppo --env LunarLander-v2 -orga luijait -f logs/
-python enjoy.py --algo ppo --env LunarLander-v2  -f logs/
-```
-
-## Training (with the RL Zoo)
-```
-python train.py --algo ppo --env LunarLander-v2 -f logs/
-# Upload the model and generate video (when possible)
-python -m rl_zoo3.push_to_hub --algo ppo --env LunarLander-v2 -f logs/ -orga luijait
-```
 
-## Hyperparameters
 ```python
-OrderedDict([('batch_size', 64),
-             ('ent_coef', 0.01),
-             ('gae_lambda', 0.98),
-             ('gamma', 0.999),
-             ('learning_rate', 0.00025),
-             ('n_epochs', 4),
-             ('n_steps', 1024),
-             ('n_timesteps', 500000),
-             ('normalize', False),
-             ('policy', 'MlpPolicy')])
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
 ```

config.json

@@ -0,0 +1 @@
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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 0x111ef7100>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x111ef71a0>", "reset_noise": "<function 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+}

ppo-LunarLander-v2/policy.optimizer.pth

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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/pytorch_variables.pth

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ppo-LunarLander-v2/system_info.txt

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+- OS: macOS-16.0-arm64-arm-64bit Darwin Kernel Version 25.0.0: Tue Jun 17 00:08:06 PDT 2025; root:xnu-12377.0.122.0.1~120/RELEASE_ARM64_T6041
+- Python: 3.12.7
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.7.1
+- GPU Enabled: False
+- Numpy: 2.3.1
+- Cloudpickle: 3.1.1
+- Gymnasium: 0.29.1

replay.mp4

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results.json

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-{"mean_reward": 116.61681770817685, "std_reward": 109.65352998263211, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-07-20T21:00:00"}
+{"mean_reward": -1070.3353720092389, "std_reward": 1311.6980823102112, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-07-20T21:54:34.970975"}