Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver,1∗ Thomas Hubert,1∗ Julian Schrittwieser,1∗ Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1 Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1 Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1 1DeepMind, 6 Pancras Square, London N1C 4AG. ∗These authors contributed equally to this work. Abstract The game of chess is the most widely-studied domain in the history of artificial intel- ligence. The strongest programs are based on a combination of sophisticated search tech- niques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforce- ment learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve,
