Seminar

Abstract: Markov decision process (MDP) is a decision making framework where a decision maker is interested in maximizing the expected discounted value of a stream of rewards received at future stages at various states which are visited according to a controlled Markov chain. Many algorithms including linear programming methods are available in the literature to compute an optimal policy when the rewards and transition probabilities are deterministic. In this talk, we consider an MDP problem where either the reward vector or the transition probability vector is a random vector. We formulate the MDP problem using distributionally robust chance-constrained optimization framework. As an application, we study a machine replacement problem and perform numerical experiments on randomly generated instances.

Short Biography: Abdel Lisser is Full Professor at the University Paris Saclay since 2001. He was heading the Graph theory and Combinatorial Optimization group at Computer Science Laboratory from 2006 to 2014. He moved to Signals and Systems Laboratory at CentraleSupelec in 2020. He was heading research group at France Telecom Research Center from 1996 to 2001. He got his Ph.D at the University of Paris Dauphine in Computer Science in 1987 and the Habilitation thesis at the University of Paris Nord in 2000. His main research area is stochastic optimization especially chance constrained optimization together with conic optimization. He also works on chance constrained stochastic games and Markov Decision Processes with chance constraints. Recently, he started working on dynamical neural networks and applications of machine learning in stochastic optimization. Applications areas are mainly energy planning optimization, autonomous vehicles and network design problems. He has already run different national and international projects in the recent years.