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cs.LG2026
Utility-Constrained Policy Optimization
Mehrdad Moghimi, Bernardo Avila Pires
Constrained MDPs (CMDPs) are a widely adopted framework for incorporating safety into RL agents; however, the framework does not support risk-sensitive constraints. This can be pro…
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…