Who to Blame? learning and control strategies with information asymmetry
Changliu Liu, Wenlong Zhang, Masayoshi Tomizuka · 2016
The rise of robot-robot interactions (RRI) is pushing for novel controller design techniques. Instead of using fixed control laws, robots should choose actions to minimize some cost functions specified by the designer. However, since the cost function of one robot may not be known to other robots (information asymmetry), special reasoning strategies are needed for multiple robots to learn to cooperate. Analysis shows that conventional learning and control strategies can lead to instability in a multi-agent system since the imperfection of other agents is not considered. In this paper, a new learning and control strategy that deals with interactions among imperfect agents is proposed. Analysis and simulation results show that the proposed strategy improves the performance of the system.