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arXiv Machine Learning By Yaniv Oren, Viliam Vadocz, Joery A. de Vries, Wendelin B\"ohmer, Matthijs T. J. Spaan, Hendrik Baier

PMCTS: Principled Parallelized Inference Time Scaling with Particle Monte Carlo Tree Search

Read the original on arXiv Machine Learning →

The paper introduces Particle MCTS (PMCTS), a parallelized version of Monte Carlo Tree Search designed for GPU acceleration and batch-parallel neural network evaluations. PMCTS preserves policy improvement guarantees of modern MCTS algorithms while scaling efficiently with parallel compute. Empirical results show that PMCTS consistently outperforms or matches heuristic-based baselines across various MCTS and reinforcement learning domains, including chess, Go, and both discrete and continuous control benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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