Lunar-v2 RL model pushed with more timestamps

LunarLander_mlp.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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LunarLander_mlp/data

@@ -42,12 +42,12 @@
         "_np_random": null
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     "n_envs": 16,
-    "num_timesteps": 507904,
-    "_total_timesteps": 500000,
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     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -56,7 +56,7 @@
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LunarLander_mlp/policy.pth

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README.md

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       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 224.17 +/- 69.82
+      value: 265.85 +/- 21.38
       name: mean_reward
       verified: false
 ---

config.json

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If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7fa638da2b90>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa638da2c20>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa638da2cb0>", "_build_mlp_extractor": "<function 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results.json

@@ -1 +1 @@
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