Approximate Dec-POMDP Solving Using Multi-Agent A*

We present an A*-based algorithm to compute policies for finite-horizon Dec-POMDPs. Our goal is to sacrifice optimality in favor of scalability for larger horizons. The main ingredients of our approach are (1) using clustered sliding window memory, (2) pruning the A* search tree, and (3) using novel A* heuristics. Our experiments show competitive performance to the state-of-the-art. Moreover, for multiple benchmarks, we achieve superior performance. In addition, we provide an A* algorithm that finds upper bounds for the optimum, tailored towards problems with long horizons. The main ingredient is a new heuristic that periodically reveals the state, thereby limiting the number of reachable beliefs. Our experiments demonstrate the efficacy and scalability of the approach.

Authors

• Wietze Koops Lund University, Lund, Sweden University of Copenhagen, Copenhagen, Denmark
• Sebastian Junges Radboud University
• Nils Jansen Ruhr University Bochum & Radboud

Keywords

• Agent-based and Multi-agent Systems: MAS: Multi-agent planning
• Planning and Scheduling: PS: Planning under uncertainty
• Planning and Scheduling: PS: POMDPs