 spectrum sensing results, the spectrum selection is performed to capture the best available spectrum to meet user needs. As a decision-making problem, it requires the system to adaptively capture the optimal choice based on observations of the environment. RL algorithms are appealing tools for designing systems that need to perform adaptive decision-making. In RL, a learner takes actions by trial and error, and learns the action patterns suitable for various situations based on the rewards obtained from these actions. Exploration actions are selected in situations even when the knowledge about the environment is uncertain. This mechanism fits into the spectrum selection problems. As shown in Figure 10.4, at the beginning of the RL cycle, the agent receives a full or partial observation of current states and the corresponding reward. Combining those states and rewards, the policy is updated by each agent during the learning stage. Then the agent performs a certain selection action based on the updated policy at the decision stage. With RL, CRN can be modeled as a distributed self-organized multi-agent system in which each SU or agent performs spectrum selection by efficiently interacting with the environment through a learning policy. In this approach, other SUs' decisions can be considered as a part of the responses of the environment for each SU. A distributed Learning Automata (LA)-based spectrum selection scheme was studied in [53]. It aimed to enable the SUs to sense the RF environment intelligently and to learn its different responses. On the other hand, the activity information of PUs and other SUs was not available, and different SUs could not exchange their information. The self-organized SUs performed the channel selection as the action and received the corresponding response indicating how favorable the action was. Based on the response, SUs could determine the optimal spectrum selection to achieve a lower transmission delay and lower interference to PUs and other SUs. State Reward Spectrum sensing Obs