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:
this https URL

使用生成式人工智能（GAI）来提高深度强化学习（DRL）算法的性能，解决样本效率低和泛化能力差的问题，通过介绍经典的 GAI 和 DRL 算法及其应用，并展示了 GAI 增强 DRL 算法的实际集成框架，提供了一个基于无人机辅助的近场 / 远场通信的案例研究来验证框架性能，并介绍了几个未来的研究方向。