Abstract:
: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 chess1 and Go2, where a perfect simulator is available. However, in real-world problems, the dynamics governing the environment are often complex and unknown. Here 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. The MuZero algorithm learns an iterable model that produces predictions relevant to planning: the action-selection policy, the value function and the reward. When evaluated on 57 different Atari games3-the canonical video game environment for testing artificial intelligence techniques, in which model-based planning approaches have historically struggled4-the MuZero algorithm achieved state-of-the-art performance. When evaluated on Go, chess and shogi-canonical environments for high-performance planning-the MuZero algorithm matched, without any knowledge of the game dynamics, the superhuman performance of the AlphaZero algorithm5 that was supplied with the rules of the game.
journal_name
Naturejournal_title
Natureauthors
Schrittwieser J,Antonoglou I,Hubert T,Simonyan K,Sifre L,Schmitt S,Guez A,Lockhart E,Hassabis D,Graepel T,Lillicrap T,Silver Ddoi
10.1038/s41586-020-03051-4subject
Has Abstractpub_date
2020-12-01 00:00:00pages
604-609issue
7839eissn
0028-0836issn
1476-4687pii
10.1038/s41586-020-03051-4journal_volume
588pub_type
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