Transposition table driven work scheduling in distributed search
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Parallel Controlled Conspiracy Number Search
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Distributed Game Tree Search on a Massively Parallel System
Data Structures and Efficient Algorithms, Final Report on the DFG Special Joint Initiative
Parallel Monte-Carlo Tree Search
CG '08 Proceedings of the 6th international conference on Computers and Games
A Twofold Distributed Game-Tree Search Approach Using Interconnected Clusters
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Monte-Carlo simulation balancing
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Reinforcement learning and simulation-based search in computer go
Reinforcement learning and simulation-based search in computer go
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
Monte-Carlo simulation balancing in practice
CG'10 Proceedings of the 7th international conference on Computers and games
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
A lock-free multithreaded monte-carlo tree search algorithm
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
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Monte-Carlo Tree Search (MCTS) is a simulation-based search method that brought about great success to applications such as Computer-Go in the past few years. The power of MCTS strongly depends on the number of simulations computed per time unit and the amount of memory available to store data gathered during simulation. High-performance computing systems such as large compute clusters provide vast computation and memory resources and thus seem to be natural targets for running MCTS. However, so far only few publications deal with parallelizing MCTS for distributed memory machines. In this paper, we present a novel approach for the parallelization of MCTS which allows for an equally distributed spreading of both the work and memory load among all compute nodes within a distributed memory HPC system. We describe our approach termed UCT-Treesplit and evaluate its performance on the example of a state-of-the-art Go engine.