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Cognitive Radio (CR) and Cooperative Communication provide key technologies for efficient utilization of available unused spectrum bands (called resources) to achieve a spectral efficient system with high throughput. But to achieve its full potential, it is essential to empower the brain of CR that is Cognitive Engine (CE), using machine learning algorithms to control the operation and adapt parameters according to the dynamic environment. However, in practical scenarios, it is difficult to formulate network model beforehand due to complex network dynamics. To address this issue, the most favorable machine learning scheme, Reinforcement Learning (RL) based schemes are proposed to empower CE without forming an explicit network model. The proposed schemes, Comparison based Cooperative Q-Learning (CCopQL) and Comparison based Cooperative State-Action-Reward-(next) State-(next) Action …