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Upload PPO LunarLander-v3 trained agent

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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - LunarLander-v3
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - stable-baselines3
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+ model-index:
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+ - name: PPO
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: LunarLander-v3
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+ type: LunarLander-v3
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+ metrics:
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+ - type: mean_reward
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+ value: 235.75 +/- 63.14
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **PPO** Agent playing **LunarLander-v3**
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+ This is a trained model of a **PPO** agent playing **LunarLander-v3**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+
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+ ## Usage (with Stable-baselines3)
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+ TODO: Add your code
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+
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+
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+ ```python
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+ from stable_baselines3 import ...
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+ from huggingface_sb3 import load_from_hub
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+
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+ ...
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+ ```
config.json ADDED
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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 0x7dca315491c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dca31549260>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dca31549300>", 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+ "__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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+ - OS: Linux-6.6.105+-x86_64-with-glibc2.35 # 1 SMP Thu Oct 2 10:42:05 UTC 2025
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+ - Python: 3.12.12
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+ - Stable-Baselines3: 2.0.0a5
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+ - PyTorch: 2.9.0+cu126
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+ - GPU Enabled: True
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+ - Numpy: 2.0.2
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+ - Cloudpickle: 3.1.2
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+ - Gymnasium: 1.2.2
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+ - OpenAI Gym: 0.25.2
replay.mp4 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:c033ad039bdf703068ba56e7395922b43bcf2fc986d128219395743772631dad
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+ size 129780
results.json ADDED
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+ {"mean_reward": 235.74914660000005, "std_reward": 63.14402667273864, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-11-26T14:29:38.244272"}