用高斯过程聚合多线程搜索结果，提升连续动作空间下的决策精度。
Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions
- 基于高斯过程回归估算未尝试动作的价值
- 6个环境中均优于现有聚合方法，推理时间增加有限
- 适合需要快速高精度决策的连续控制场景
蒙特卡洛树搜索是在线规划的核心算法，其根部并行变体在限定时钟时间但追求最优性能时广泛应用。在连续动作空间环境中，如何最优地聚合不同线程的统计信息是一个重要但未充分探索的问题。本文提出一种新方法，利用高斯过程回归对未在环境中试过的有潜力动作进行价值估计。我们在6个不同领域进行了系统评估，结果表明该方法优于现有聚合策略，且仅带来适度的推理时间增加。
原文摘要 · Abstract (English)
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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