Keyword

monte carlo tree search

2 papers tagged “monte carlo tree search”

AINature · Oct 2017

Mastering the game of Go without human knowledge

David Silver, Julian Schrittwieser, Karen Simonyan and Demis Hassabis

This paper presented AlphaGo Zero, which learned to play Go solely through self-play reinforcement learning without any human game data or handcrafted features, using a single neural network and a simpler tree search. Starting from random play, it discovered Go knowledge and novel strategies on its own. AlphaGo Zero surpassed all previous versions of AlphaGo, including the one that beat Lee Sedol.

reinforcement learning self-play monte carlo tree search alphago zero

AINature · Jan 2016

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

David Silver, Aja Huang, Chris J. Maddison and Demis Hassabis

This paper introduced AlphaGo, a system combining deep convolutional neural networks (policy and value networks) trained by supervised learning from human games and reinforcement learning by self-play, integrated with Monte Carlo tree search. The networks reduce the breadth and depth of the search needed to evaluate Go positions. AlphaGo defeated other Go programs and became the first program to beat a professional human Go player (Fan Hui) on a full-size board.

deep learning reinforcement learning monte carlo tree search game of go