Entropic Risk-Aware Monte Carlo Tree Search

2026-01-25 · Pedro P. Santos, Jacopo Silvestrin, Alberto Sardinha, Francisco S. Melo arxiv

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.

📄 PDF Abstract BibTeX arXiv:2601.17667

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Risk-Aware General-Utility Markov Decision Processes

2026-07-10 · Pedro P. Santos, Fábio Vital, Alberto Sardinha, Francisco S. Melo arxiv

We study general-utility Markov decision processes (GUMDPs) with risk-aware objectives. In this framework, an agent aims to optimize a risk measure of the distribution of objective values, where the objective function de…

Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning

2022-11-23 · Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley 외

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from a single execution of a policy. In these settings, making decisions based on the average future returns is n…

Multi-Objective Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search

2021-02-01 · Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley 외

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy. In these settings, making decisions based on the average future returns is…

Decision Making Multi-Objective Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Cooperative Trajectory Planning in Uncertain Environments with Monte Carlo Tree Search and Risk Metrics

2022-03-09 · Philipp Stegmaier, Karl Kurzer, J. Marius Zöllner

Automated vehicles require the ability to cooperate with humans for smooth integration into today's traffic. While the concept of cooperation is well known, developing a robust and efficient cooperative trajectory planni…

Trajectory Planning

Tail-Risk-Safe Monte Carlo Tree Search under PAC-Level Guarantees

2025-08-07 · Zuyuan Zhang, Arnob Ghosh, Tian Lan arxiv

Making decisions with respect to just the expected returns in Monte Carlo Tree Search (MCTS) cannot account for the potential range of high-risk, adverse outcomes associated with a decision. To this end, safety-aware MCT…