This is the pre-proceedings for the RLC 2026. You may expect minor changes.
Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
Will be presented at the Reinforcement Learning Conference (RLC), Montréal, Quebec, Canada, August 15–17, 2026.
Keywords: Markov Decision Processes, online planning, Monte Carlo tree search, parallel MCTS,
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.
Junlin Xiao, Victor-Alexandru Darvariu, Bruno Lacerda, and Nick Hawes. "Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions." Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
BibTeX:@article{xiao2026gaussian,
title={Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions},
author={Junlin Xiao and Victor-Alexandru Darvariu and Bruno Lacerda and Nick Hawes},
journal={Reinforcement Learning Journal},
volume={7},
pages={},
year={2026}
}