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arXiv · 2601.17667

Entropic Risk-Aware Monte Carlo Tree Search

Pedro P. Santos·Jacopo Silvestrin·Alberto Sardinha·Francisco S. Melo
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Abstract

We propose a provably correct Monte Carlo tree search (MCTS) algorithm for solving risk-aware Markov decision processes (MDPs) with entropic risk measure (ERM) objectives. We provide a non-asymptotic analysis of our proposed algorithm, showing that the algorithm: (i) is correct in the sense that the empirical ERM obtained at the root node converges to the optimal ERM; and (ii) enjoys polynomial regret concentration. Our algorithm successfully exploits the dynamic programming formulations for solving risk-aware MDPs with ERM objectives introduced by previous works in the context of an upper confidence bound-based tree search algorithm. Finally, we provide a set of illustrative experiments comparing our risk-aware MCTS method against relevant baselines.

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Pedro P. Santos, Jacopo Silvestrin, Alberto Sardinha, Francisco S. Melo. 2026-01-25. Entropic Risk-Aware Monte Carlo Tree Search. https://arxiv.org/abs/2601.17667

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