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

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.

Deep Learning - Chessprogramming wiki

Computer chess - Wikipedia

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

AlphaZero: DeepMind's New Chess AI

Electronics, Free Full-Text

PDF] Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting

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

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] The Chess Transformer: Mastering Play using Generative Language Models

What does AI's success playing complex board games tell brain scientists?