New model for AntBulletEnv-v0
Browse files- .gitattributes +1 -0
- README.md +1 -1
- a2c-AntBulletEnv-v0.zip +2 -2
- a2c-AntBulletEnv-v0/data +6 -6
- a2c-AntBulletEnv-v0/policy.optimizer.pth +1 -1
- a2c-AntBulletEnv-v0/policy.pth +1 -1
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
- vec_normalize.pkl +1 -1
.gitattributes
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replay.mp4 filter=lfs diff=lfs merge=lfs -text
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README.md
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type: AntBulletEnv-v0
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metrics:
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- type: mean_reward
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-
value:
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name: mean_reward
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verified: false
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---
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type: AntBulletEnv-v0
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metrics:
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- type: mean_reward
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value: 855.88 +/- 62.66
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name: mean_reward
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verified: false
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---
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a2c-AntBulletEnv-v0.zip
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a2c-AntBulletEnv-v0/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: 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 0x7f6bbbcf6280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6bbbcf6310>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6bbbcf63a0>", 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It allows to keep variance\n above zero and prevent it from growing too fast. 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