Wireless sensor networking is a viable communication technology among low-cost and energy-limited sensor nodes deployed in an environment. Due to high oper-ational features, the application area of this technology is extended significantly but with some energy related challenges. One main cause of the nodes energy wasting in these net-works is idle listening characterized with no communication activity. This drawback can be mitigated by themeans of energy-efficient multiple access control schemes so as tominimize idle listening. In this paper, we discuss the applicability of distributed learning algorithms namely reinforcement learning towardsmultiple access control (MAC) inwireless sensor net-works. We perform a comparative review of relevant work in the literature and then present a cooperative multi agent reinforcement learning framework for MAC design in wireless sensor networks. Accordingly, the paper concludes with some major challenges and open issues of distributed MAC design using reinforcement learning
