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.

我们提出了一种基于 A * 算法的有限时域 Dec-POMDPs 策略计算方法，目标是以可扩展性为代价来牺牲最优性，在较大的时域内取得竞争性性能，主要特点是使用聚类的滑动窗口内存、修剪 A * 搜索树以及使用新颖的 A * 启发式策略。我们的实验结果显示与最先进的方法相比有竞争力的性能，并且在多个基准测试中取得了更优异的表现。此外，我们还提供了一种基于 A * 算法的找到最优值上界的方法，适用于长时域问题。主要特点是定期揭示状态的新启发式方法，从而限制可达置信度的数量。我们的实验证明了该方法的有效性和可扩展性。