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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…
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…
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…
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…
Encoded Value-at-Risk: A Predictive Machine for Financial Risk Management
Hamidreza Arian, Mehrdad Moghimi, Ehsan Tabatabaei +1
Measuring risk is at the center of modern financial risk management. As the world economy is becoming more complex and standard modeling assumptions are violated, the advanced arti…