# Approximate Dec-POMDP Solving Using Multi-Agent A\* Source: [Approximate Dec-POMDP Solving Using Multi-Agent A\*]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:42:27.853Z Authors: Wietze Koops, Sebastian Junges, Nils Jansen Published: 2024-05-09T10:33:07.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Dec-POMDP Planning on COOPERATIVE BOX PUSHING](): Dec-POMDP Planning; COOPERATIVE BOX PUSHING; Cumulative Reward: 2433.51. - [Dec-POMDP Planning on DEC TIGER](): Dec-POMDP Planning; DEC TIGER; Cumulative Reward: 170.91. - [Dec-POMDP Planning on RECYCLING ROBOTS](): Dec-POMDP Planning; RECYCLING ROBOTS; Cumulative Reward: 6154.94. - [Dec-POMDP Planning on MARS ROVERS](): Dec-POMDP Planning; MARS ROVERS; Cumulative Reward: 234.06. ## Original sources and implementation [Original paper]() [PDF]()