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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17667 |
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| _version_ | 1866918324210761728 |
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| author | Santos, Pedro P. Silvestrin, Jacopo Sardinha, Alberto Melo, Francisco S. |
| author_facet | Santos, Pedro P. Silvestrin, Jacopo Sardinha, Alberto Melo, Francisco S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17667 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Entropic Risk-Aware Monte Carlo Tree Search Santos, Pedro P. Silvestrin, Jacopo Sardinha, Alberto Melo, Francisco S. Machine Learning 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. |
| title | Entropic Risk-Aware Monte Carlo Tree Search |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.17667 |