Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

«The AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go».

Source : [1712.01815] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm