Approximate solution of infinite-horizon risk-sensitive Markov decision processes

Infinite-horizon risk-sensitive Markov decision processes (MDPs) under the discounted cost criterion are challenging to solve because the optimal policy may be non- stationary. Existing solution methods reformulate the problem as a continuous-state (risk-neutral) MDP and solve it using state-discretization or value function approximation. Such approaches typically lack explicit stopping conditions or error bounds. In this … Read more

Numerical Methods for Convex Multistage Stochastic Optimization

Optimization problems involving sequential decisions in  a  stochastic environment    were studied  in  Stochastic Programming (SP), Stochastic Optimal Control  (SOC) and Markov Decision Processes (MDP). In this paper we mainly concentrate on SP and  SOC modelling   approaches. In these frameworks there are natural situations  when the considered problems are  convex. Classical approach to sequential optimization … Read more