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

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TLDR
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.
Abstract
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.

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References
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Journal ArticleDOI

Mastering the game of Go with deep neural networks and tree search

TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Journal ArticleDOI

Mastering the game of Go without human knowledge

TL;DR: An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.
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In-Datacenter Performance Analysis of a Tensor Processing Unit

TL;DR: This paper evaluates a custom ASIC-called a Tensor Processing Unit (TPU)-deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN) and compares it to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the samedatacenters.
Journal ArticleDOI

Deep Blue

TL;DR: Deep Blue as discussed by the authors is the chess machine that defeated then-reigning World Chess Champion Garry Kasparov in a six-game match in 1997 and won the first World Chess Championship.
Journal ArticleDOI

An analysis of alpha-beta pruning

TL;DR: The alpha-beta procedure for searching game trees is shown to be optimal in a certain sense, and bounds are obtained for its running time with various kinds of random data.
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