Upload tuned PPO LunarLander-v2 trained agent, again，again

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 272.19 +/- 20.72
+      value: 275.82 +/- 19.11
       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 0x782d6f9c8d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x782d6f9c8dc0>", "reset_noise": 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+    "normalize_advantage": true,
+    "target_kl": null,
     "observation_space": {
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@@ -69,7 +84,7 @@
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@@ -77,23 +92,8 @@
         "_np_random": null
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-    "n_steps": 1024,
-    "gamma": 0.999,
-    "gae_lambda": 0.98,
-    "ent_coef": 0.01,
-    "vf_coef": 0.5,
-    "max_grad_norm": 0.5,
-    "batch_size": 64,
-    "n_epochs": 4,
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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

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 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.3.1+cu121
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 - GPU Enabled: True
 - Numpy: 1.26.4
 - Cloudpickle: 2.2.1

results.json

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