Co-evolutionary perception-based reinforcement learning for sensor allocation in autonomous vehicles

In this paper we study the problem of sensor allocation in Unmanned Aerial Vehicles (UAVs). Each UAV uses perception-based rules for generalizing decision strategy across similar states and reinforcement learning for adapting these rules to the uncertain, dynamic environment. A big challenge for reinforcement learning algorithms in this problem is that UAVs need to learn two complementary policies: how to allocate their individual sensors to appearing targets and how to distribute themselves as a team in space to match the density and importance of targets underneath. We address this problem using a co-evolutionary approach, where the policies are learned separately, but they use a common reward function. The applicability of our approach to the UAV domain is verified using a high-fidelity robotic simulator. Based on our results, we believe that the co-evolutionary reinforcement learning approach to reducing dimensionality of the action space presented in this paper is general enough to be applicable to many other multi-objective optimization problems, particularly those that involve a tradeoff between individual optimality and team-level optimality.