# Parallelization of Monte Carlo Tree Search in Continuous Domains

arXiv: https://arxiv.org/abs/2003.13741 · HTML: https://mlsanity.com/paper/2003.13741/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions](https://mlsanity.com/paper/2512.09727/) (2025)
- [Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving](https://mlsanity.com/paper/2602.08334/) (2026)

## Abstract (arXiv)

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.