Figure 1 from Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Por um escritor misterioso
Last updated 26 abril 2025

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"

Resource Management for Internet of Things Environments
Create AI for your Own Board Game From Scratch — AlphaZero-Part 3, by Haryo Akbarianto Wibowo

All the important games artificial intelligence has conquered - TechTalks

Acquisition of chess knowledge in AlphaZero

MuZero - Wikipedia

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

MuZero figures out chess, rules and all

Reinforcement learning applied to games

Shogi - Wikipedia

Frontiers AlphaZe∗∗: AlphaZero-like baselines for imperfect information games are surprisingly strong

Discovering faster matrix multiplication algorithms with reinforcement learning

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

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

Reinforcement Learning, Fast and Slow: Trends in Cognitive Sciences

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