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In Cognitive Radio (CR), the conventional narrow band spectrum sensing requires either random channel sensing order or predefined channel sensing sequence to sense all channels in a specified spectrum band in order to detect vacant channels. This may be inefficient in energy constraint devices networks such as Cognitive Radio Wireless Sensor Network (CR-WSN). In this paper, we propose a Reinforcement Learning based clustered Cooperative Channel Sensing (RL-CCS) that learns channels’ dynamic behaviors in terms of channel availability, sensing energy cost, and channel impairment to achieve optimal sensing sequence and optimal set of channels. The problem of selecting optimal policy is formulated as a Markov Decision Problem (MDP) to determine optimal solutions that minimize sensing energy while improving Primary User (PU) detection and channel utilization in CR-WSN. Simulation results …