Heuristics for multiagent reinforcement learning in decentralized decision problems
Martin Allen, David Hahn, Douglas C. MacFarland · 2014
Decentralized partially observable Markov decision processes (Dec-POMDPs) model cooperative multiagent scenarios, providing a powerful general framework for team-based artificial intelligence. While optimal algorithms exist for Dec-POMDPs, theoretical and empirical results demonstrate that they are impractical for many problems of real interest. We examine the use of reinforcement learning (RL) as a means to generate adequate, if not optimal, joint policies for Dec-POMDPs. It is easily demonstrated (and expected) that single-agent RL produces results of little joint utility. We therefore investigate heuristic methods, based upon the dynamics of the Dec-POMDP formulation, that bias the learning process to produce coordinated action. Empirical tests on a benchmark problem show that these heuristics significantly enhance learning performance, even out-performing a hand-crafted heuristic in cases where the learning process converges quickly.