A Meta-MDP Approach for Information Gathering Heterogeneous Multi-agent Systems

In this paper, we address the problem of heterogeneous multi-robot cooperation for information gathering and situation evaluation in a stochastic and partially observable environment. The goal is to optimally gather information about targets in the environment with several robots having different capabilities. The classical Dec-POMDP framework is a good tool to compute an optimal joint policy for such problems. However, its scalability is weak. To overcome this limitation, we developed a Meta-MDP model with actions being individual policies of information gathering based on POMDPs. We compute an optimal exploration policy for each couple of robot and target, and the Meta-MDP model acts as a long-term optimal task allocation algorithm. We experiment our model on a simulation environment and compare to an optimal MPOMDP approach and show promising results on solution quality and scalability.