Approximate planning for decentralized MDPs with sparse interactions
Francisco S. Melo, Manuela Veloso · Adaptive Agents and Multi-Agents Systems · 2010
We explore how local interactions can simplify the process of decision-making in multiagent systems. We review decentralized sparse-interaction Markov decision process [3] that explicitly distinguishes the situations in which the agents in the team must coordinate from those in which they can act independently. We situate this class of problems within different multiagent models, such as MMDPs and transition independent Dec-MDPs [2]. We contribute new algorithm for efficient planning in this class of problems. We provide empirical comparisons between our algorithms and other existing algorithms for this class of problems.