Initial commit
Browse files- README.md +1 -1
- a2c-AntBulletEnv-v0.zip +2 -2
- a2c-AntBulletEnv-v0/data +8 -8
- a2c-AntBulletEnv-v0/policy.optimizer.pth +1 -1
- a2c-AntBulletEnv-v0/policy.pth +1 -1
- config.json +1 -1
- results.json +1 -1
- vec_normalize.pkl +2 -2
README.md
CHANGED
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@@ -16,7 +16,7 @@ model-index:
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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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| 17 |
metrics:
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| 18 |
- type: mean_reward
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| 19 |
+
value: 2990.83 +/- 22.47
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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/data
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":type:": "<class 'numpy.ndarray'>",
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":type:": "<class 'numpy.ndarray'>",
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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 0x7ff991d59000>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff991d59090>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff991d59120>", 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results.json
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