sed. A Q-learning-based online policy was developed first to deal with the underlying Markov process without any prior knowledge. An infinite horizon stochastic dynamic programming-based optimal online policy was then proposed by assuming that the full statistical knowledge of the governing Markov process was known. Finally, a generalized Benders decomposition algorithm-based offline policy was given, where the energy arrivals and channel states information were known before all transmitters for a given time deadline. By combining Multi-Armed Bandit (MAB) and matching theory, the ML-assisted Oppor- tunistic Spectrum Access (OSA) approach was developed in [241]. A single SU case was first considered without the volatility of channel availability information. Next, the upper confi- dence bound algorithm-based Occurrence-Aware OSA (OA-OSA) framework was designed to achieve the long-term optimal network throughput performance and the trade-off between exploration and exploitation. The OA-OSA was then extended to the multi-SU scenario with channel access competitions by integrating the Gale-Shapley algorithm. In [115], by considering the OFDMA-based resource allocation for the underlying SUs, researchers aimed to minimize the weighted sum of the secondary interference power under the constraints of QoS, power consumption, and data rate. A Damped Three-Dimensional (D3D) Message-Passing Algorithm (MPA) based on DL was pro- posed, and an analogous back-propagation algorithm was developed to learn the optimal parameters. A sub-optimal resource allocation method was developed based on a damped two-dimensional MPA to improve computational efficiency. By considering the EE and SE, as well as Computing Efficiency (CE) for both PUs and SUs, a DL-based resource allocation algorithm in CRNs was proposed in [113] to minimize the weighted sum of the secondary interference power. It was shown that the proposed scheme significantly improved both the SE and EE for PUs and SUs. Insufficient specificity and function approximation can i