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

arXiv: https://arxiv.org/abs/2512.09727 · HTML: https://mlsanity.com/paper/2512.09727/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 1 of 1)

- [Parallelization of Monte Carlo Tree Search in Continuous Domains](https://mlsanity.com/paper/2003.13741/) (2020)

## Built on by (most cited first, 1 of 1)

- [Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders](https://mlsanity.com/paper/2602.19315/) (2026)

## Abstract (arXiv)

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.