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Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective insolving decentralized POMDPs with large horizons. We generalize the algorithmand improve its scalability by reducing the complexity with respect to thenumber of observations from exponential to polynomial. We derive error boundson solution quality with respect to this new approximation and analyze theconvergence behavior. To evaluate the effectiveness of the improvements, weintroduce a new, larger benchmark problem. Experimental results show thatdespite the high complexity of decentralized POMDPs, scalable solutiontechniques such as MBDP perform surprisingly well.

Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs