  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.
