Initial commit
Browse files- README.md +1 -1
- a2c-AntBulletEnv-v0.zip +2 -2
- a2c-AntBulletEnv-v0/data +17 -17
- a2c-AntBulletEnv-v0/policy.optimizer.pth +2 -2
- a2c-AntBulletEnv-v0/policy.pth +2 -2
- a2c-AntBulletEnv-v0/system_info.txt +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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type: AntBulletEnv-v0
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metrics:
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---
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type: AntBulletEnv-v0
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metrics:
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value: 1833.67 +/- 155.85
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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/system_info.txt
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@@ -2,6 +2,6 @@
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- Python: 3.10.12
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- Stable-Baselines3: 1.8.0
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- PyTorch: 2.0.1+cu118
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- GPU Enabled:
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- Numpy: 1.22.4
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- Gym: 0.21.0
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- Python: 3.10.12
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- Stable-Baselines3: 1.8.0
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- PyTorch: 2.0.1+cu118
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- GPU Enabled: True
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- Numpy: 1.22.4
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- Gym: 0.21.0
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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: 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 0x7f70bb76f880>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f70bb76f910>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f70bb76f9a0>", 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It allows to keep variance\n above zero and prevent it from growing too fast. 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