Markov Decision Processes with Average‐Value‐at‐Risk criteria

Markov Decision Processes with Average‐Value‐at‐Risk criteria

0.00 Avg rating0 Votes
Article ID: iaor201111661
Volume: 74
Issue: 3
Start Page Number: 361
End Page Number: 379
Publication Date: Dec 2011
Journal: Mathematical Methods of Operations Research
Authors: ,
Keywords: markov processes
Abstract:

We investigate the problem of minimizing the Average‐Value‐at‐Risk (AVaR τ ) of the discounted cost over a finite and an infinite horizon which is generated by a Markov Decision Process (MDP). We show that this problem can be reduced to an ordinary MDP with extended state space and give conditions under which an optimal policy exists. We also give a time‐consistent interpretation of the AVaR τ . At the end we consider a numerical example which is a simple repeated casino game. It is used to discuss the influence of the risk aversion parameter τ of the AVaR τ ‐criterion.

Reviews

Required fields are marked *. Your email address will not be published.