Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

TL;DR

This paper explores how generative AI can be integrated with deep reinforcement learning to address limitations like low sample efficiency and poor generalization, demonstrating a novel framework and validating it through a UAV communication case study.

Contribution

It introduces a new GAI-enhanced DRL framework, combining generative AI with reinforcement learning to improve performance and generalization capabilities.

Findings

GAI-enhanced DRL improves sample efficiency

Framework validated on UAV communication case study

Provides future research directions

Abstract

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.