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.

翻译：作为一种基于交互式学习的人工智能技术，深度强化学习已在诸多领域得到广泛应用并取得了显著成就。然而，深度强化学习仍面临样本效率低、泛化能力差等局限性。为此，本文阐述了如何利用生成式人工智能解决上述问题并提升深度强化学习算法的性能。我们首先介绍了几种经典的生成式人工智能与深度强化学习算法，并展示了生成式人工智能增强的深度强化学习算法的应用实例。随后，从数据与策略两个维度探讨了如何利用生成式人工智能改进深度强化学习算法。进而，提出了一个展现生成式人工智能与深度强化学习实际融合的创新框架——生成式人工智能增强的深度强化学习。此外，通过无人机辅助集成近场/远场通信的案例研究验证了所提框架的性能。最后，本文指出了若干未来研究方向。相关代码已开源：https://xiewenwen22.github.io/GAI-enhanced-DRL。