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Challenges for implementing Monte Carlo Tree Search in commercial games

Matthew Bedder

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Disclaimer

This is pitched at people with little/no knowledge of the area

(Sorry, AI people)

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“A game is a series of interesting choices”

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Monte Carlo Tree Search

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Monte Carlo Tree SearchOnly generate the bits of the game tree we need

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MCTS – the basics

Iteratively builds a decision tree

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MCTS Selection

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MCTS Expansion

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MCTS Simulation

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Monte Carlo evaluationsPerforming random actions gives us a (weak) approximation of state values

X O X OX

OX

O

X

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MCTS Simulation

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MCTS Backpropagation

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MCTS – the basics

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Applications of MCTS

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MCTS applications

• Amazons• Arimaa• Connect 4• Dou Di Zhu• Go variants• Havannah

• Hex• Morpion Solitaire• Othello• Settlers of Catan• Spades• ...

Many board and card games, including:

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MCTS applicationsAll of the strongest AI players of Go use variants of MCTS

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MCTS applicationsCurrently there are very limited applications to commercial video games or other domains

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http://www.aifactory.co.uk/AIF_Games_Spades.htm

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MCTS Successes

http://aigamedev.com/open/coverage/mcts-rome-ii/

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http://gwaredd.github.io/nuclai_mcts

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Challenges for MCTS

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Games are hard

There are as many states of Civilization II as there are atoms in the

observable universe 10,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,

000,000,000,000,000,000,000,000,000,000 times over

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Computational costThis is actually important

Game AIs (usually) are limited to time sliced CPU

AlphaGo used• 1,920 CPUs and 280 GPUs• ~1MW

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Monte Carlo Tree Search optimisationsGenerally either:

Domain specificor

Overly generic

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Monte Carlo Tree Search optimisationsReplacing random simulations helps… usually

Adding prior knowledge helps… usually

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Our approachDomain-specific optimisations for arbitrary domains

I’d love to talk about our work during/after coffee

www-users.cs.york.ac.uk/~beddermatthew@bedder.co.uk@bedder

Matthew Bedder