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

David Silver1,2, Thomas Hubert3, Julian Schrittwieser3

  • 1DeepMind, 6 Pancras Square, London N1C 4AG, UK. davidsilver@google.com dhcontact@google.com.

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A new artificial intelligence algorithm, AlphaZero, uses reinforcement learning to achieve superhuman performance in games like chess and Go. It learns from self-play without human domain knowledge, defeating top programs from random play.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Game Theory

Background:

  • Chess has been a long-standing benchmark for artificial intelligence (AI) research.
  • Current AI chess programs rely on human expertise and handcrafted evaluation functions.
  • Recent advances in reinforcement learning (RL) have shown promise in complex games like Go.

Purpose of the Study:

  • To develop a generalized AI algorithm capable of achieving superhuman performance across multiple challenging games.
  • To demonstrate the efficacy of reinforcement learning from self-play without domain-specific knowledge.

Main Methods:

  • The study introduces the AlphaZero algorithm, a unified approach generalizing the AlphaGo Zero methodology.
  • AlphaZero utilizes deep neural networks and Monte Carlo tree search, trained via reinforcement learning from self-play.
  • The algorithm is provided only with the rules of the game, starting from random play.

Main Results:

  • AlphaZero achieved superhuman performance in chess, shogi, and Go.
  • The algorithm convincingly defeated world-champion level AI programs in these games.
  • This demonstrates a significant advancement in generalized game-playing AI.

Conclusions:

  • Reinforcement learning from self-play is a powerful paradigm for developing superhuman AI in complex strategic domains.
  • AlphaZero represents a significant step towards more general artificial intelligence systems.
  • The approach removes the need for extensive domain-specific engineering in AI game development.