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

Geng Sun, Wenwen Xie, Dusit Niyato, Fang Mei, Jiawen Kang, Hongyang Du, Shiwen Mao

TL;DR

The paper tackles the limitations of deep reinforcement learning, notably sample inefficiency and weak generalization, by integrating generative AI models. It surveys key GAI techniques (GAN, diffusion, VAE, Transformer), DRL algorithms (DQN, DDPG, TD3, PPO, SAC), and their synergy, then presents a framework for GAI-enhanced DRL from data and policy perspectives. A UAV-based near-field/far-field communication case study demonstrates that GAI-enabled approaches, especially diffusion-based policy learning (GDM), can markedly improve rewards, data efficiency, and decision quality, albeit with higher computational costs. The work highlights practical future directions, including efficiency gains, reward design with LLMs, transfer learning, and broader exploration of GAI models to extend DRL performance in complex, real-world settings.

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.

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

TL;DR

The paper tackles the limitations of deep reinforcement learning, notably sample inefficiency and weak generalization, by integrating generative AI models. It surveys key GAI techniques (GAN, diffusion, VAE, Transformer), DRL algorithms (DQN, DDPG, TD3, PPO, SAC), and their synergy, then presents a framework for GAI-enhanced DRL from data and policy perspectives. A UAV-based near-field/far-field communication case study demonstrates that GAI-enabled approaches, especially diffusion-based policy learning (GDM), can markedly improve rewards, data efficiency, and decision quality, albeit with higher computational costs. The work highlights practical future directions, including efficiency gains, reward design with LLMs, transfer learning, and broader exploration of GAI models to extend DRL performance in complex, real-world settings.

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.
Paper Structure (33 sections, 4 figures, 2 tables)

This paper contains 33 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Overview of GAI and DRL
  3. GAI Models
  4. DRL Algorithms
  5. Applications of GAI-enhanced DRL
  6. GAI-enhanced DRL
  7. From Data Perspective
  8. Data Augmentation
  9. Adaptation to variable state
  10. Feature Extraction
  11. Environment Simulation
  12. From Policy Perspective
  13. Improved Policy Network
  14. Multimodal Learning
  15. Handling hybrid action
  16. ...and 18 more sections

Figures (4)

  • Figure 1: Summary of typical GAI models.
  • Figure 2: Summary of data and policy performance improved by GAI in DRL.
  • Figure 3: Proposed Framework of GAI-enhanced DRL on UAV-assisted integrated near-field/far-field communication.
  • Figure 4: The convergence curves of rewards, rates, and power consumption, as well as the training time, of the case study under three different action spaces. Specifically, the results in continuous action space are shown in (a), (b), (c) and (d), respectively. The results in discrete action space are shown in (e), (f), (g) and (h), respectively. The results in hybrid action space are shown in (i), (j), (k) and (l), respectively.