2 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.LG2024
Robust Reinforcement Learning with Dynamic Distortion Risk Measures
Anthony Coache, Sebastian Jaimungal
In a reinforcement learning (RL) setting, the agent's optimal strategy heavily depends on her risk preferences and the underlying model dynamics of the training environment. These…