Multi-agent planning with baseline regret minimization

Abstract

We propose a novel baseline regret minimization algorithm for multi-agent planning problems modeled as finite-horizon decentralized POMDPs. It guarantees to produce a policy that is provably at least as good as a given baseline policy. We also propose an iterative belief generation algorithm to efficiently minimize the baseline regret, which only requires necessary iterations so as to converge to the policy with minimum baseline regret. Experimental results on common benchmark problems confirm the benefits of the algorithm compared with the state-of-the-art approaches.