  As a form of artificial intelligence (AI) technology based on interactive
learning, deep reinforcement learning (DRL) has been widely applied across
various fields and has achieved remarkable accomplishments. However, DRL faces
certain limitations, including low sample efficiency and poor generalization.
Therefore, we present how to leverage generative AI (GAI) to address these
issues above and enhance the performance of DRL algorithms in this paper. We
first introduce several classic GAI and DRL algorithms and demonstrate the
applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to
improve DRL algorithms from the data and policy perspectives. Subsequently, we
introduce a framework that demonstrates an actual and novel integration of GAI
with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the
framework on UAV-assisted integrated near-field/far-field communication to
validate the performance of the proposed framework. Moreover, we present
several future directions. Finally, the related code is available at:
https://xiewenwen22.github.io/GAI-enhanced-DRL.
