Cooperative Game of Energy-Constrained Agents in Wireless Communication Systems through Reinforcement Learning

Abstract

Security issues are always considered in systems with wireless networks. However, few of them investigated covert signals existing on different communication channels to confuse advisories. In this paper, we consider the cooperation between two energy-constrained agents, who could inject covert signals. First, the system performance is measured by Kullback–Leibler divergence (KLD) to avoid much deviation. Then, the cooperative game between two agents is considered, in which two agents share the common goal at confusing advisories. More formally, this cooperative game is formulated as a Markov decision process (MDP) and the most economic strategies are obtained through reinforcement learning (RL) under the imperfect information. Finally, the feasibility of theoretical results is demonstrated on the interconnected New England test system (NETS) as well as its reduced system.