mpose some limitations on RL algorithms, but neural networks can be used to compensate for them. DRL algorithms are capable of combining the process of RL with deep neural networks to approximate the Q action-value function. Compared with conventional RL, DRL can significantly improve learning performance and learning speed. DRL has attracted a lot of attention in research for solving the problems in CR networks such as resource allocation, spectrum management, and power control. In [161], the authors presented a DRL-based resource allocation method for CRN to max- imize the secondary network performance while meeting the primary link interference constraint. By adopting a Mean Opinion Score (MOS) as the performance metric, the pro- posed model seamlessly integrates resource allocations among heterogeneous traffic. The resource allocation problem was solved by utilizing a Deep Q Network (DQN) algorithm where a neural network approximated the Q action-value function. TL was incorporated into the learning procedure to further improve the learning performance. It was shown that TL reduced the number of iterations for convergence by approximately 25% and 72% compared to the DQN algorithm without utilizing TL or standard Q-learning, respectively. 10.2 ML-Based Methodologies for SS 10.2.1.4 Spectrum Handoff Spectrum handoff is intended to maintain seamless communication during the transition to a better spectrum. However, enabling spectrum handoff for multimedia applications in a CRN is challenging due to multiple interruptions from PUs, contentions among SUs, and heterogeneous Quality-of-Experience (QoE) requirements. Although an SU may not know exactly when the PU comes back, it always wants to achieve reliable spectrum usage to support the QoS requirements. If the quality of the current channel degrades, the SU can make one of the following three decisions: (1) Stay in the same channel and wait for it to become idle again (called stay-and-wait). (2) Stay in the same channel and adapt to the varying channel condition