Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 272.38 +/- 14.21
+      value: 240.10 +/- 41.51
       name: mean_reward
       verified: false
 ---

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 0x7bfbacbc9090>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7bfbacbc9120>", "reset_noise": 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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/data

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         "__module__": "stable_baselines3.common.policies",
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-- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.2.1+cu121
+- PyTorch: 2.3.0+cu121
 - GPU Enabled: True
 - Numpy: 1.25.2
 - Cloudpickle: 2.2.1

results.json

@@ -1 +1 @@
-{"mean_reward": 272.3795683082214, "std_reward": 14.20548615776984, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-16T11:25:00.289137"}
+{"mean_reward": 240.10072293213352, "std_reward": 41.508408520661305, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-22T23:25:34.346820"}