Upload PPO LunarLander-v2 trained agent

README.md

@@ -6,7 +6,7 @@ tags:
 - reinforcement-learning
 - stable-baselines3
 model-index:
-- name: PPO-test
+- name: PPO
   results:
   - task:
       type: reinforcement-learning
@@ -16,13 +16,13 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -161.07 +/- 54.22
+      value: 264.72 +/- 26.43
       name: mean_reward
       verified: false
 ---
 
-# **PPO-test** Agent playing **LunarLander-v2**
-This is a trained model of a **PPO-test** agent playing **LunarLander-v2**
+# **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)

config.json

@@ -1 +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 0x793ee8c57eb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x793ee8c57f40>", "reset_noise": 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ppo-LunarLander-v2/policy.pth

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results.json

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-{"mean_reward": -161.06745494778005, "std_reward": 54.217111861334864, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-01-13T09:22:10.459806"}
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