PDF] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Por um escritor misterioso
Last updated 05 julho 2024
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.
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Electronics, Free Full-Text
GitHub - Nicolas-Maurer/Onitama_AlphaZero: Implementation of the AlphaZero algorithm for the game Onitama
Reinforcement Learning: A Quick Overview, by Mohit Pilkhan
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Is AlphaZero really a scientific breakthrough in AI?, by Jose Camacho Collados
Deepmind's AlphaZero Plays Chess
Reinforcement learning applied to games
PDF) A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
AlphaZero: A General Reinforcement Learning Algorithm that Masters Chess, Shogi and Go through Self-Play
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