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

2025/12/10 by Junlin Xiao, Victor-Alexandru Darvariu, Xiao, Junlin +5
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence in Games #Gaussian Processes and Bayesian Inference #cs.AI

paper · pdf · doi:10.48550/arxiv.2512.09727

openalex publication_date 2025/12/10 · openalex created_date 2025/12/12 · openalex updated_date 2026/07/28

Abstract

Monte Carlo Tree Search is a cornerstone algorithm for online planning, and its root-parallel variant is widely used when wall clock time is limited but best performance is desired. In environments with continuous action spaces, how to best aggregate statistics from different threads is an important yet underexplored question. In this work, we introduce a method that uses Gaussian Process Regression to obtain value estimates for promising actions that were not trialed in the environment. We perform a systematic evaluation across 6 different domains, demonstrating that our approach outperforms existing aggregation strategies while requiring a modest increase in inference time.

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