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README.md
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type: CartPole-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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---
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## Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is a simple **MuZero** implementation to OpenAI/Gym/Box2d **CartPole-v0** by using [DI-engine](https://github.com/opendilab/di-engine) and [LightZero](https://github.com/opendilab/LightZero).
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**LightZero** is
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## Model Usage
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### Install the Dependencies
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git clone https://github.com/opendilab/huggingface_ding.git
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pip3 install -e ./huggingface_ding/
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# install environment dependencies if needed
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-
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```
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</details>
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type: CartPole-v0
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metrics:
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- type: mean_reward
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value: 198.6 +/- 4.2
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name: mean_reward
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---
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## Model Description
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<!-- Provide a longer summary of what this model is. -->
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This implementation applies **MuZero** to the OpenAI/Gym/Box2d **CartPole-v0** environment using [LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine).
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**LightZero** is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers.
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## Model Usage
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### Install the Dependencies
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git clone https://github.com/opendilab/huggingface_ding.git
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pip3 install -e ./huggingface_ding/
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# install environment dependencies if needed
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pip3 install DI-engine[common_env,video]
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pip3 install LightZero
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```
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</details>
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