  Decentralized (PO)MDPs provide an expressive framework for sequential
decision making in a multiagent system. Given their computational complexity,
recent research has focused on tractable yet practical subclasses of
Dec-POMDPs. We address such a subclass called CDEC-POMDP where the collective
behavior of a population of agents affects the joint-reward and environment
dynamics. Our main contribution is an actor-critic (AC) reinforcement learning
method for optimizing CDEC-POMDP policies. Vanilla AC has slow convergence for
larger problems. To address this, we show how a particular decomposition of the
approximate action-value function over agents leads to effective updates, and
also derive a new way to train the critic based on local reward signals.
Comparisons on a synthetic benchmark and a real-world taxi fleet optimization
problem show that our new AC approach provides better quality solutions than
previous best approaches.
