Distributed Monte Carlo Tree Search: A Novel Technique and its Application to Computer Go

L. Schaefers, M. Platzner
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引用次数: 16

Abstract

Monte Carlo tree search (MCTS) has brought about great success regarding the evaluation of stochastic and deterministic games in recent years. We present and empirically analyze a data-driven parallelization approach for MCTS targeting large HPC clusters with Infiniband interconnect. Our implementation is based on OpenMPI and makes extensive use of its RDMA based asynchronous tiny message communication capabilities for effectively overlapping communication and computation. We integrate our parallel MCTS approach termed UCT-Treesplit in our state-of-the-art Go engine Gomorra and measure its strengths and limitations in a real-world setting. Our extensive experiments show that we can scale up to 128 compute nodes and 2048 cores in self-play experiments and, furthermore, give promising directions for additional improvement. The generality of our parallelization approach advocates its use to significantly improve the search quality of a huge number of current MCTS applications.
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分布式蒙特卡罗树搜索:一种新技术及其在计算机围棋中的应用
蒙特卡洛树搜索(MCTS)近年来在随机和确定性博弈的评估方面取得了巨大的成功。我们提出并实证分析了一种针对具有Infiniband互连的大型HPC集群的MCTS数据驱动并行化方法。我们的实现基于OpenMPI,并广泛利用其基于RDMA的异步微消息通信功能来有效地重叠通信和计算。我们将称为UCT-Treesplit的并行MCTS方法整合到我们最先进的围棋引擎Gomorra中,并在现实世界中测量其优势和局限性。我们广泛的实验表明,我们可以在自玩实验中扩展到128个计算节点和2048个核心,此外,还为进一步改进提供了有希望的方向。我们的并行化方法的通用性提倡使用它来显著提高大量当前MCTS应用程序的搜索质量。
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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