mountain car
Browse files- README.md +6 -6
- config.json +1 -1
- ppo-MountainCarContinuous-v0.zip +3 -0
- ppo-MountainCarContinuous-v0/_stable_baselines3_version +1 -0
- ppo-MountainCarContinuous-v0/data +105 -0
- ppo-MountainCarContinuous-v0/policy.optimizer.pth +3 -0
- ppo-MountainCarContinuous-v0/policy.pth +3 -0
- ppo-MountainCarContinuous-v0/pytorch_variables.pth +3 -0
- ppo-MountainCarContinuous-v0/system_info.txt +9 -0
- replay.mp4 +2 -2
- results.json +1 -1
README.md
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---
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library_name: stable-baselines3
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tags:
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-
-
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name:
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type:
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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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# **PPO** Agent playing **
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This is a trained model of a **PPO** agent playing **
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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---
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library_name: stable-baselines3
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tags:
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- MountainCarContinuous-v0
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: MountainCarContinuous-v0
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type: MountainCarContinuous-v0
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metrics:
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- type: mean_reward
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value: -0.00 +/- 0.00
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name: mean_reward
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verified: false
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---
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# **PPO** Agent playing **MountainCarContinuous-v0**
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This is a trained model of a **PPO** agent playing **MountainCarContinuous-v0**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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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 0x7f53074f3420>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f53074f34c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f53074f3560>", 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It allows to keep variance\n above zero and prevent it from growing too fast. 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ppo-MountainCarContinuous-v0/policy.optimizer.pth
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