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Parallelization of Monte Carlo Tree Search in Continuous Domains

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arxiv 2003.13741 v1 pith:CVBS6SG5 submitted 2020-03-30 cs.LG cs.AIstat.ML

Parallelization of Monte Carlo Tree Search in Continuous Domains

Karl Kurzer , Christoph H\"ortnagl , J. Marius Z\"ollner This is my paper
classification cs.LG cs.AIstat.ML
keywords parallelizationcontinuousmctsdomainscarlochallengingmonteroot
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Monte Carlo Tree Search (MCTS) has proven to be capable of solving challenging tasks in domains such as Go, chess and Atari. Previous research has developed parallel versions of MCTS, exploiting today's multiprocessing architectures. These studies focused on versions of MCTS for the discrete case. Our work builds upon existing parallelization strategies and extends them to continuous domains. In particular, leaf parallelization and root parallelization are studied and two final selection strategies that are required to handle continuous states in root parallelization are proposed. The evaluation of the resulting parallelized continuous MCTS is conducted using a challenging cooperative multi-agent system trajectory planning task in the domain of automated vehicles.

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Cited by 2 Pith papers

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  1. Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving

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    By decomposing QMDP belief trees into independent scenario trees and vectorizing tree expansion and collision checks, Vec-QMDP plans in milliseconds on CPUs and matches or beats state-of-the-art nuPlan planners.

  2. Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions

    cs.AI 2025-12 conditional novelty 6.0

    GPR2P uses Gaussian Process regression to interpolate action values across threads in root-parallel MCTS, allowing selection of untried actions and improving average performance in six continuous-action domains.