3 papers
cs.LG2026
Decoupling Time and Risk: Risk-Sensitive Reinforcement Learning with General Discounting
Mehrdad Moghimi, Anthony Coache, Hyejin Ku
Distributional reinforcement learning (RL) is a powerful framework increasingly adopted in safety-critical domains for its ability to optimize risk-sensitive objectives. However, t…
cs.LG2025
Risk-sensitive Actor-Critic with Static Spectral Risk Measures for Online and Offline Reinforcement Learning
Mehrdad Moghimi, Hyejin Ku
The development of Distributional Reinforcement Learning (DRL) has introduced a natural way to incorporate risk sensitivity into value-based and actor-critic methods by employing r…
cs.LG2025
Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning
Mehrdad Moghimi, Hyejin Ku
In domains such as finance, healthcare, and robotics, managing worst-case scenarios is critical, as failure to do so can lead to catastrophic outcomes. Distributional Reinforcement…