抄録
In this paper, we investigate the game theoretic coverage control whose objective is to lead agents to optimal configurations over a mission space. In particular, the objective of this paper is to achieve the control objective (i) in the absense of the perfect prior knowledge on importance of each point and (ii) in the presence of the action constraints. For this purpose, we first formulate coverage problems with two different global objective functions as so-called potential games. Then, we present a payoff-based learning algorithm determining actions based only on the past actual outcomes. The feature of the present algorithm is to allow an agent to take an irrational action. We also clarify a relation between a design parameter of the algorithm and theprobability which agents take the optimal actions and prove that the probability can be arbitrarily increased. Then, we demonstrate the effectiveness of the present algorithm through experiments on a testbed.
