Mizhaan Prajit Maniyar

Policy Newton Methods for Distortion Riskmetrics

We consider the problem of risk-sensitive control in a reinforcement learning (RL) framework. In particular, we aim to find a risk-optimal policy by maximizing the distortion riskmetric (DRM) of the discounted reward in a finite-horizon Markov decision process (MDP). DRMs are a rich class of risk me