New AlphaZero Paper Explores Chess Variants
Por um escritor misterioso
Last updated 22 dezembro 2024
In a new paper from DeepMind, this time co-written by 14th world chess champion Vladimir Kramnik, the self-learning chess engine AlphaZero is used to explore the design of different variants of the game of chess, with different sets of rules. The paper is titled Assessing Game Balance with AlphaZero
Reimagining Chess with AlphaZero, February 2022
AlphaZero: A General Reinforcement Learning Algorithm that Masters Chess, Shogi and Go through Self-Play
PDF) Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test Environments
Acquisition of Chess Knowledge in AlphaZero – arXiv Vanity
Chess: Models, code, and papers - CatalyzeX
Mastering Atari, Go, chess and shogi by planning with a learned model
DeepMind's AlphaZero AI Helps Design New Chess Rules, by Chintan Trivedi, deepgamingai
Google AI Achieves Alien Superhuman Mastery of Chess and Go in Mere Hours - The New Stack
Machine Learning Spotlight gallery – Weights & Biases
Opening book based on Leela (AlphaZero) policy data : r/chess
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