Figure 1 from Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Figure 1: Training AlphaZero for 700,000 steps. Elo ratings were computed from evaluation games between different players when given one second per move. a Performance of AlphaZero in chess, compared to 2016 TCEC world-champion program Stockfish. b Performance of AlphaZero in shogi, compared to 2017 CSA world-champion program Elmo. c Performance of AlphaZero in Go, compared to AlphaGo Lee and AlphaGo Zero (20 block / 3 day) (29). - "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm"

Electronics, Free Full-Text

Reinforcement learning is all you need, for next generation language models.

Alessandro Vespignani on X: A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play “a program called AlphaZero, which taught itself to play Go, chess, and shogi” /

GitHub - zjeffer/chess-deep-rl: Research project: create a chess engine using Deep Reinforcement Learning

Mastering the game of Go with deep neural networks and tree search

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

Learning in continuous action space for developing high dimensional potential energy models

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

MuZero figures out chess, rules and all

Frontiers Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data

Algorithms, Free Full-Text