Game Theoretic Control for Robot Teams
In the real world, noisy sensors and limited communication make it difficult for robot teams to coordinate in tightly coupled tasks. Team members cannot simply apply single-robot solution techniques for partially observable problems in parallel because they do not take into account the recursive effect that reasoning about the beliefs of others has on policy generation. Instead, we must turn to a game theoretic approach to model the problem correctly. Partially observable stochastic games (POSGs) provide a solution model for decentralized robot teams, however, this model quickly becomes intractable. In previous work we presented an algorithm for lookahead search in POSGs. Here we present an extension which reduces computation during lookahead by clustering similar observation histories together. We show that by clustering histories which have similar profiles of predicted reward, we can greatly reduce the computation time required to solve a POSG while maintaining a good approximation to the optimal policy. We demonstrate the power of the clustering algorithm in a real-time robot controller as well as for a simple benchmark problem.