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

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

What exactly makes the greatest players of all time, such as Magnus Carlsen, Bobby Fischer, and Garry Kasparov stand out from the rest? The basic

Mastering Chess Logic

papers

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

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

PDF] Giraffe: Using Deep Reinforcement Learning to Play Chess

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

Mastering the game of Go without human knowledge