Markov decision evolutionary game theoretic learning for cooperative sensing of unmanned aerial vehicles

As one of the major contributions of biology to competitive decision making, evolutionary game theory provides a useful tool for studying the evolution of cooperation. To achieve the optimal solution for unmanned aerial vehicles (UAVs) that are carrying out a sensing task, this paper presents a Markov decision evolutionary game (MDEG) based learning algorithm. Each individual in the algorithm follows a Markov decision strategy to maximize its payoff against the well known Tit-for-Tat strategy. Simulation results demonstrate that the MDEG theory based approach effectively improves the collective payoff of the team. The proposed algorithm can not only obtain the best action sequence but also a sub-optimal Markov policy that is independent of the game duration. Furthermore, the paper also studies the emergence of cooperation in the evolution of self-regarded UAVs. The results show that it is the adaptive ability of the MDEG based approach as well as the perfect balance between revenge and forgiveness of the Tit-for-Tat strategy that the emergence of cooperation should be attributed to.