Algorithmes de co-évolution pour la résolution approchée de PDM multi-agent

Abstract

Markov Decision Processes (MDP) provide a formal approach for solving sequential decision problems under uncertainty. Decentralized Markov Decision Processes extend MDP. They are used to model the problem of several agents making decision under uncertainty. However, solving a decentralized markov decision process is NEXP-complete as soon as two agents are involved. In this paper we propose algorithms for solving approximately Multi-agent MDP problems in a centralized or decentralized way. The aim of our methods is to design a system in which agents coordinate to achieve a task in collaboration. Coordination is based on two major properties: subjectivity and empathy. While subjectivity allows an agent to deal with incomplete and local perceptions, thus to design memory less policies, empathy allows an agent to adapt its decisions in regards with the uncertainty of the decisions of the others.