Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -6.97 +/- 2.00
+      value: -0.37 +/- 0.17
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

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a2c-PandaReachDense-v2/data

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     "n_steps": 5,
     "gamma": 0.99,
-    "gae_lambda": 1.0,
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     "ent_coef": 0.0,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,

a2c-PandaReachDense-v2/policy.optimizer.pth

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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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7f3b6dca3d30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3b6dca5900>"}, "verbose": 1, 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results.json

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