Approximate Dec-POMDP Solving Using Multi-Agent A*
This work addresses scalability challenges in multi-agent decision-making under uncertainty, representing an incremental improvement with novel heuristics for longer horizons.
The authors tackled the problem of solving finite-horizon Dec-POMDPs by developing an A*-based algorithm that sacrifices optimality for scalability, achieving competitive or superior performance on multiple benchmarks compared to state-of-the-art methods.
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.