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

2024-05-31

arXiv.org

AI Summary

To address the low sample efficiency and poor cross-scenario generalization of deep reinforcement learning (DRL), this paper proposes the first generative AI–enhanced unified DRL framework, jointly optimizing data generation and policy modeling. The framework seamlessly integrates diffusion models (for high-fidelity simulation environment modeling and data augmentation), large language models (for task understanding and policy prior injection), and mainstream DRL algorithms (e.g., PPO and SAC), augmented by policy distillation and end-to-end joint training. Evaluated on UAV-assisted communication tasks, the framework achieves a 2.3× speedup in training convergence and reduces cross-scenario performance variance by 67%, significantly enhancing generalization and robustness. All code and benchmark case studies are publicly released.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Computer Vision: Diffusion Models for Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Enhance DRL with generative AI
• Improve sample efficiency in DRL
• Boost generalization of DRL algorithms

Innovation

• Integrates generative AI with DRL
• Enhances DRL sample efficiency
• Improves DRL generalization capabilities