Parallel Monte-Carlo Tree Search

Authors:
Guillaume M. Chaslot;Mark H. Winands;H. Jaap Herik
Affiliations:
Games and AI Group, MICC, Faculty of Humanities and Sciences, Universiteit Maastricht, Maastricht, The Netherlands;Games and AI Group, MICC, Faculty of Humanities and Sciences, Universiteit Maastricht, Maastricht, The Netherlands;Games and AI Group, MICC, Faculty of Humanities and Sciences, Universiteit Maastricht, Maastricht, The Netherlands
Venue:
CG '08 Proceedings of the 6th international conference on Computers and Games
Year:
2008

Citing 2
Cited 16

Efficient selectivity and backup operators in Monte-Carlo tree search

CG'06 Proceedings of the 5th international conference on Computers and games
Bandit based monte-carlo planning

ECML'06 Proceedings of the 17th European conference on Machine Learning

Intelligent agents for the game of go

IEEE Computational Intelligence Magazine
Job-level proof-number search for connect6

CG'10 Proceedings of the 7th international conference on Computers and games
On the scalability of parallel UCT

CG'10 Proceedings of the 7th international conference on Computers and games
Scalability and parallelization of Monte-Carlo tree search

CG'10 Proceedings of the 7th international conference on Computers and games
A Monte-Carlo AIXI approximation

Journal of Artificial Intelligence Research
Multi-agent Monte Carlo Go

The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Parallel Monte-Carlo tree search for HPC systems

Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
A lock-free multithreaded monte-carlo tree search algorithm

ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Evaluation function based monte-carlo LOA

ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Creating an upper-confidence-tree program for havannah

ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Randomized parallel proof-number search

ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Bandit-Based genetic programming

EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Parallel monte carlo tree search scalability discussion

AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Single-player Monte-Carlo tree search for SameGame

Knowledge-Based Systems
Monte-Carlo tree search parallelisation for computer go

Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference
TEXPLORE: real-time sample-efficient reinforcement learning for robots

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Monte-Carlo Tree Search (MCTS) is a new best-first search method that started a revolution in the field of Computer Go. Parallelizing MCTS is an important way to increase the strength of any Go program. In this article, we discuss three parallelization methods for MCTS: leaf parallelization, root parallelization, and tree parallelization. To be effective tree parallelization requires two techniques: adequately handling of (1) local mutexesand (2) virtual loss. Experiments in 13×13 Go reveal that in the program Mangoroot parallelization may lead to the best results for a specific time setting and specific program parameters. However, as soon as the selection mechanism is able to handle more adequately the balance of exploitation and exploration, tree parallelization should have attention too and could become a second choice for parallelizing MCTS. Preliminary experiments on the smaller 9×9 board provide promising prospects for tree parallelization.