Approximate Dec-POMDP Solving Using Multi-Agent A*

Approximate Dec-POMDP Solving Using Multi-Agent A*

Wietze Koops, Sebastian Junges, Nils Jansen

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 6743-6751. https://doi.org/10.24963/ijcai.2024/745

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