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.
By Yaniv Oren, Viliam Vadocz, Joery A. de Vries, Wendelin B\"ohmer, Matthijs T. J. Spaan, Hendrik Baier
arXiv:2608. 15700v1 Announce Type: new Abstract: Background: Distillation of training targets generated thru search/planning has proven useful in reinforcement learning, but search can take exceedingly long.
By Gavin B. Rens
The paper introduces a robust variant of Monte Carlo Tree Search that addresses ambiguities in transition dynamics and reward distributions, bridging the gap between simulation-based planning and real-world deployment. It incorporates a robust power mean backup operator and exploration bonuses to guarantee finite-sample convergence at every node, achieving an ≠O(n−1/2) convergence rate for root value estimation comparable to standard MCTS. Empirical results demonstrate robust performance in planning tasks even under significant model mismatches.
By Tuan Dam, Kishan Panaganti, Brahim Driss, Adam Wierman
arXiv:2609.09094v1 Announce Type: new
Abstract: Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever...
By Raphael Boige, Amine Boumaza, Bruno Scherrer
The article argues that Monte Carlo Tree Search (MCTS) and every‑visit Monte Carlo (MC) control are essentially the same method, differing only in terminology and presentation. It shows that MCTS’s four stages—selection, expansion, simulation, and backup—can be reduced to two core operations: sampling trajectories under the current policy and performing every‑visit MC updates. The note aims to make this equivalence explicit and easier to recognize.
By Xianyi Wu
arXiv:2606. 05296v1 Announce Type: new Abstract: LLM agents operate in two distinct regimes: open-weight agents amenable to reinforcement learning (RL) and black-box agents whose behaviour must be controlled purely at test time.
By Dae Yon Hwang, Raunaq Suri, Valentin Villecroze, Anthony L. Caterini, Jesse C. Cresswell, No\"el Vouitsis, Brendan Leigh Ross
arXiv:2607. 26946v1 Announce Type: new Abstract: Recent advancements in Computer Go, driven by AlphaZero and MuZero, rely heavily on Monte Carlo Tree Search (MCTS) to correct the errors of the neural network policy.
By Mehrad Yaghoubi, Azam Bastanfard, Abbas Jalilvand, Ashkan Rezaei
arXiv:2606. 05021v1 Announce Type: new Abstract: We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm.
By Marc Walden, Jason Liu, Shaashwath Sivakumar, Ryan Liu, Hamza Khan
arXiv:2609.37447v1 Announce Type: cross
Abstract: How strong can an AlphaZero-style chess system become under limited training compute when its entire learning loop is engineered for efficiency? We t...
By Bertil Braun
arXiv:2512. 09727v2 Announce Type: replace Abstract: 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.
By Junlin Xiao, Victor-Alexandru Darvariu, Bruno Lacerda, Nick Hawes
arXiv:2606. 01708v1 Announce Type: cross Abstract: We study fixed-confidence best-action identification (BAI) in stochastic minimax trees.
By Peter Chen, Xi Chen
arXiv:2606. 15247v1 Announce Type: cross Abstract: The asymptotic behaviour of Monte Carlo Exploring Starts (MCES) is a long-standing open question in reinforcement learning, even in the tabular setting.
By Octave Oliviers, Glenn Vinnicombe