Approximate Dec-POMDP Solving Using Multi-Agent A*
<|reference_start|>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.<|reference_end|>
@article{koops2024approximate,
    title={Approximate Dec-POMDP Solving Using Multi-Agent A*},
    author={Wietze Koops, Sebastian Junges, Nils Jansen},
    journal={arXiv preprint arXiv:2405.05662},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05662},
    primaryClass={cs.AI}
}