PDF] Mastering Chess and Shogi by Self-Play with a General

This paper generalises the approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains, and convincingly defeated a world-champion program in each case. The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, 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 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, and convincingly defeated a world-champion program in each case.

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

Mastering Atari, Go, chess and shogi by planning with a learned model

AlphaZero

PDF] Mastering Terra Mystica: Applying Self-Play to Multi-agent Cooperative Board Games

papers

Mastering Atari, Go, chess and shogi by planning with a learned model

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

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

PDF] The Chess Transformer: Mastering Play using Generative Language Models

Full article: Time management in a chess game through machine learning