arXiv:2607. 05359v1 Announce Type: new Abstract: Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding.
By Idan Lev-Yehudi, Vadim Indelman
Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding. Tree-based search methods such as Monte Carlo Tree Search (MCTS) remain popular, but their branching structure can require sampling budgets that grow exponentially with lookahead depth in the worst case.
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:2609.06489v1 Announce Type: cross
Abstract: Monte Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapt...
By Tuan Dam
arXiv:2605.08982v4 Announce Type: replace
Abstract: Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement and action selection in Reinforcement Learning. Due to its sequenti...
By Yaniv Oren, Viliam Vadocz, Joery A. de Vries, Wendelin B\"ohmer, Matthijs T. J. Spaan, Hendrik Baier
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