Recursive Monte Carlo Tree Search Algorithm Shows Major Speed Gains

Global: New Recursive Monte Carlo Tree Search Algorithm Promises Significant Speed Gains

Researchers have unveiled a recursive Monte‑Carlo tree search (MCTS) method, termed RMCTS, that claims to accelerate search processes compared with the AlphaZero‑style MCTS‑UCB approach. The study reports speed improvements of more than 40‑fold for single‑root searches and roughly three‑fold for batch searches of root states, while maintaining comparable policy quality.

Algorithmic Design

RMCTS differs from traditional MCTS‑UCB by exploring the search tree in a breadth‑first order, enabling large‑batch neural network inferences that reduce GPU latency. The recursion computes optimized posterior policies at each node, working from leaf nodes upward, based on the regularized policy optimization framework introduced by Grill et al.

Tree Construction

Unlike adaptive tree expansion in MCTS‑UCB, RMCTS follows prior network policies to define its tree structure. The authors acknowledge this as a limitation but argue that the resulting speed advantage outweighs the drawback in practice.

Performance Evaluation

Experimental results presented in the paper compare RMCTS with MCTS‑UCB across three board games: Connect‑4, Dots‑and‑Boxes, and Othello. The authors state that RMCTS‑trained networks achieve similar quality to those trained with MCTS‑UCB while requiring roughly one‑third of the training time.

Implications for AI Training

If the reported speed gains translate to broader domains, RMCLS could reduce computational costs for reinforcement‑learning pipelines that rely on extensive tree search, potentially accelerating development cycles for game‑playing agents and other decision‑making systems.

Limitations and Future Work

The paper notes that the fixed tree definition may limit exploration efficiency in certain scenarios. Ongoing research may focus on hybrid approaches that combine breadth‑first batching with adaptive tree growth to balance speed and search depth.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.