Initial commit
Browse files- README.md +1 -1
- a2c-AntBulletEnv-v0.zip +2 -2
- a2c-AntBulletEnv-v0/data +7 -7
- a2c-AntBulletEnv-v0/policy.optimizer.pth +2 -2
- a2c-AntBulletEnv-v0/policy.pth +1 -1
- config.json +1 -1
- replay.mp4 +2 -2
- results.json +1 -1
- vec_normalize.pkl +1 -1
README.md
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value:
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name: mean_reward
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task:
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type: reinforcement-learning
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results:
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- type: mean_reward
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value: 1596.61 +/- 177.30
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name: mean_reward
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task:
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type: reinforcement-learning
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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 0x7f79ab3f6950>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f79ab3f69e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f79ab3f6a70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f79ab3f6b00>", "_build": "<function 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