  Information theoretic sensor management approaches are an ideal solution to
state estimation problems when considering the optimal control of multi-agent
systems, however they are too computationally intensive for large state spaces,
especially when considering the limited computational resources typical of
large-scale distributed multi-agent systems. Reinforcement learning (RL) is a
promising alternative which can find approximate solutions to distributed
optimal control problems that take into account the resource constraints
inherent in many systems of distributed agents. However, the RL training can be
prohibitively inefficient, especially in low-information environments where
agents receive little to no feedback in large portions of the state space. We
propose a hybrid information-driven multi-agent reinforcement learning (MARL)
approach that utilizes information theoretic models as heuristics to help the
agents navigate large sparse state spaces, coupled with information based
rewards in an RL framework to learn higher-level policies. This paper presents
our ongoing work towards this objective. Our preliminary findings show that
such an approach can result in a system of agents that are approximately three
orders of magnitude more efficient at exploring a sparse state space than naive
baseline metrics. While the work is still in its early stages, it provides a
promising direction for future research.
