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

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 some 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. Firstly, we 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 the data and policy performance of DRL algorithms. Subsequently, we propose a novel framework that describes the technical details of GAI-enhanced DRL. Additionally, a case study on UAV-assisted integrated near-field/far-field communication is constructed to validate the performance of the proposed framework. Moreover, we present several future directions.

The Proposed Framework for GAI-enhanced DRL.

Our proposed framework consists of four parts.
Part A (GAN-enhanced GAI): We enhance the critic network of DRL by using GAN. Specifically, the generator network outputs estimated action values, while the target generator network obtains the target action values. The discriminator network attempts to minimize the distance between the estimated action values and the target action values calculated by the Bellman operator.
Part B (VAE-enhanced GAI): We use VAE to reduce the dimensionality of the high-dimensional state space to reduce the computational complexity issue in DRL. In this case, we train the VAE with data and use the decoder to extract representations of the state space, which are then used as inputs for the actor and critic networks. Additionally, VAE can construct a latent representation space for continuous parameters conditioned on state and embedding of discrete actions to handle hybrid actions.
Part C (Transformer-enhanced GAI): We enhance the actor network of DRL by using Transformer. Specifically, we replace the Multi-Layer Perceptron (MLP) with a network based on the attention mechanism of Transformer to analyze the current state in the environment.
Part D (GDM-enhanced GAI): We improve the policy network of DRL by employing the reverse process of GDM. Specifically, we treat the policy network as a denoiser, progressively adding denoising noise to the initial Gaussian noise to recover or discover the optimal actions.

The Experiment Result of GAI-enhanced TD3.

This figure shows the convergence curves of four GAI models-enhanced TD3 algorithm in different types of action space.
We can observe that GDM-based TD3 achieves the best performance compared to other GAI models.
This is because GDM can accurately capture the underlying data distribution, which provides a more effective representation of the environment. Moreover, the unique structure of GDM, which involves a diffusion process, offers a more stable and efficient learning process.

Run the Program

1) Create a new conda environment with the following command:


      conda create --name GAIDRL python==3.10

2) Activate the created environment with the following command:


      conda activate GAIDRL

3) Install the following packets using pip:


      pip install gym==0.26.2
      pip install torch==2.2.2
      pip install matplotlib==3.8.4
      pip install numpy==1.26.4
      pip install scipy==1.13.0

4) Run the different algorithm:


      GAN-enhanced TD3: run GAN_TD3_simple.py;
      VAE-enhanced TD3: run VAE_TD3.py;
      Transformer-enhanced TD3: run Attention_TD3_double.py;
      GDM-enhanced TD3: run mainDM3.py.

BibTeX

@article{sun2024,
        title={Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases},
        author={Geng Sun, Wenwen Xie, Dusit Niyato, Fang Mei, Jiawen Kang, Hongyang Du, Shiwen Mao},
        journal={arXiv preprint arXiv:2405.20568},
        year={2024}
      }