Recent scaling up of decentralized partially observable Markov decision process (DECPOMDP) solvers towards realistic applications is mainly due to approximate methods. Of this family, MEMORY BOUNDED DYNAMIC PROGRAMMING (MBDP), which combines in a suitable manner top-down heuristics and bottomup value function updates, can solve DECPOMDPs with large horizons. The performance of MBDP, however, ca...