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"

Applied Sciences, Free Full-Text

AlphaZero: A General Reinforcement Learning Algorithm that Masters Chess, Shogi and Go through Self-Play

Algorithms, Free Full-Text

Figure 1 from Giraffe: Using Deep Reinforcement Learning to Play Chess

Mastering construction heuristics with self-play deep reinforcement learning

Reinforcement learning applied to games

Mastering chess and shogi by self-play with a general reinforcement learning algorithm

AlphaGomoku: An AlphaGo-based Gomoku Artificial Intelligence using Curriculum Learning – arXiv Vanity

Giraffe: Using Deep Reinforcement Learning to Play Chess – arXiv Vanity