Another commit

PandaReachDenseA2C-n8.zip

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PandaReachDenseA2C-n8/_stable_baselines3_version

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PandaReachDenseA2C-n8/data

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PandaReachDenseA2C-n8/policy.optimizer.pth

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PandaReachDenseA2C-n8/policy.pth

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PandaReachDenseA2C-n8/pytorch_variables.pth

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PandaReachDenseA2C-n8/system_info.txt

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+- OS: Linux-5.15.0-60-generic-x86_64-with-glibc2.35 # 66-Ubuntu SMP Fri Jan 20 14:29:49 UTC 2023
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -3.06 +/- 1.19
+      value: -1.66 +/- 0.20
       name: mean_reward
       verified: false
 ---

config.json

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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 0x7ff44dc85dc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff44dc87ac0>"}, "verbose": 1, 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results.json

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vec_normalize.pkl

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