init test

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 283.05 +/- 17.02
+      value: 285.24 +/- 16.44
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
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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 0x7f322edd9e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f322edd9ee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f322edd9f70>", "_build_mlp_extractor": "<function 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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. 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results.json

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         "__module__": "stable_baselines3.common.policies",
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+        ":serialized:": 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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -99,7 +100,7 @@
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 256,
+    "batch_size": 1024,
     "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",

thicc-ppo-LunarLander-rc/policy.optimizer.pth

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