Upload PPO LunarLander-v2 trained agent

.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:
+- 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: 242.98 +/- 34.61
+      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 0x109c449a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x109c44a40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x109c44ae0>", 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ppo-LunarLander-v2.zip

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+oid sha256:bf0ae0a21b457c65cef9c793c70cca9d9c0385d92c224d571cc74fd357152003
+size 148686

ppo-LunarLander-v2/_stable_baselines3_version

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+2.0.0a5

ppo-LunarLander-v2/data

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+        "__firstlineno__": 390,
+        "__doc__": "\nPolicy class for actor-critic algorithms (has both policy and value prediction).\nUsed 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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+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x109c44a40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x109c44ae0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x109c44b80>",
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+        "forward": "<function ActorCriticPolicy.forward at 0x109c44cc0>",
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+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x109c44f40>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x109c44fe0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x109c45080>",
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+        ],
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ppo-LunarLander-v2/system_info.txt

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+- OS: macOS-15.6.1-arm64-arm-64bit-Mach-O Darwin Kernel Version 24.6.0: Mon Jul 14 11:30:34 PDT 2025; root:xnu-11417.140.69~1/RELEASE_ARM64_T8103
+- Python: 3.13.4
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.8.0
+- GPU Enabled: False
+- Numpy: 2.3.2
+- Cloudpickle: 3.1.1
+- Gymnasium: 0.28.1

replay.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6d60c508ca6138e15e3ad44814b5ca996a7fc436de3d8800b58351ce8c793aa7
+size 180147

results.json

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+{"mean_reward": 242.9787788215837, "std_reward": 34.6137894715336, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-08-26T21:08:30.885733"}