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

Silver, D., Hubert, Schrittwieser, Antonoglou, Lai, Guez, Lanctot, et al. (2017). Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815.

Type

Preprint

Intellectual base

Grey literature

Year

2017

Link

https://arxiv.org/abs/1712.01815 not authoritativefound

Cited from

operations_research

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Markov Decision Processes (MDPs)
  • Silver et al. (2017b) extended the same self-play algorithm to chess and shogi, and the approach has since been further extended (MuZero 2019 handles unknown game dynamics; AlphaStar 2019 handles StarCraft II as a multi-agent partially-observable game; AlphaProof/AlphaGeometry 2024 handle mathematical-theorem-proving).

    This sourceAlphaZero paper extending the AlphaGo Zero algorithm to chess and shogi, demonstrating a single MDP-RL algorithm achieving superhuman play in three games with rules-only knowledge.

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