  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.
