Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.

本文提出了MuZero算法，它通过结合基于树的搜索和学习模型实现了对一系列具有挑战性且视觉复杂的领域的超人类表现，在57款不同的Atari游戏上以及对Go、国际象棋和象棋等游戏的测试中，都表现出了与AlphaZero算法相当的性能，而无需知道游戏规则。

通过学习模型规划，掌握雅达利、围棋、国际象棋和将棋