Average-Reward Decentralized Markov Decision Processes

Formal analysis of decentralized decision making has become a thriving research area in recent years, producing a number of multi-agent extensions of Markov decision processes. While much of the work has focused on optimizing discounted cumulative reward, optimizing average reward is sometimes a more suitable criterion. We formalize a class of such problems and analyze its characteristics, showing that it is NP complete and that optimal policies are deterministic. Our analysis lays the foundation for designing two optimal algorithms. Experimental results with a standard problem from the literature illustrate the applicability of these solution techniques.

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Marek Petrik, Shlomo Zilberstein
• Keywords: Artificial Intelligence, Decentralized Decision Making, IJCAI 2007, Markov Decision Processes, Thriving Research Area