Decoupling Time and Risk: Risk-Sensitive Reinforcement Learning with General Discounting

• URL: http://arxiv.org/abs/2602.04131v1
• Date: Wed, 04 Feb 2026 01:49:12 GMT
• Title: Decoupling Time and Risk: Risk-Sensitive Reinforcement Learning with General Discounting
• Authors: Mehrdad Moghimi, Anthony Coache, Hyejin Ku,
• Abstract summary: We propose a novel framework that supports flexible discounting of future rewards and optimization of risk measures in distributional RL.<n>Our results highlight that discounting is a cornerstone in decision-making problems for capturing more expressive temporal and risk preferences profiles.
• Score: 2.179313476241343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributional reinforcement learning (RL) is a powerful framework increasingly adopted in safety-critical domains for its ability to optimize risk-sensitive objectives. However, the role of the discount factor is often overlooked, as it is typically treated as a fixed parameter of the Markov decision process or tunable hyperparameter, with little consideration of its effect on the learned policy. In the literature, it is well-known that the discounting function plays a major role in characterizing time preferences of an agent, which an exponential discount factor cannot fully capture. Building on this insight, we propose a novel framework that supports flexible discounting of future rewards and optimization of risk measures in distributional RL. We provide a technical analysis of the optimality of our algorithms, show that our multi-horizon extension fixes issues raised with existing methodologies, and validate the robustness of our methods through extensive experiments. Our results highlight that discounting is a cornerstone in decision-making problems for capturing more expressive temporal and risk preferences profiles, with potential implications for real-world safety-critical applications.

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