Lunar Agent Training 1

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -236.41 +/- 132.30
+      value: 259.13 +/- 20.60
       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 0x7f4b4adc24c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4b4adc2550>", "reset_noise": 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"
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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 8,
+    "_n_updates": 256,
     "n_steps": 2048,
     "gamma": 0.9995,
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ppo-LunarLander-v2312/policy.optimizer.pth

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results.json

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