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Offline and Distributional Reinforcement Learning for Radio Resource Management

Eslam Eldeeb, Hirley Alves

TL;DR

This work tackles the practical challenges of applying reinforcement learning to radio resource management by adopting offline and distributional RL. The authors formulate RRM as an MDP with a PF-based objective and develop Conservative Quantile Regression (CQR), which combines Conservative Q-Learning and QR-DQN to learn from static data while modeling return distributions. The results show that CQR outperforms baseline schemes and even online RL in the RRM context, demonstrating improved sum-rate and 5th-percentile fairness and highlighting data-efficiency with smaller offline datasets. The approach offers a safer, more robust path to intelligent wireless control for 6G-era networks and can be extended to other optimization tasks such as beamforming and IRS-assisted communications.

Abstract

Reinforcement learning (RL) has proved to have a promising role in future intelligent wireless networks. Online RL has been adopted for radio resource management (RRM), taking over traditional schemes. However, due to its reliance on online interaction with the environment, its role becomes limited in practical, real-world problems where online interaction is not feasible. In addition, traditional RL stands short in front of the uncertainties and risks in real-world stochastic environments. In this manner, we propose an offline and distributional RL scheme for the RRM problem, enabling offline training using a static dataset without any interaction with the environment and considering the sources of uncertainties using the distributions of the return. Simulation results demonstrate that the proposed scheme outperforms conventional resource management models. In addition, it is the only scheme that surpasses online RL with a 10 % gain over online RL.

Offline and Distributional Reinforcement Learning for Radio Resource Management

TL;DR

This work tackles the practical challenges of applying reinforcement learning to radio resource management by adopting offline and distributional RL. The authors formulate RRM as an MDP with a PF-based objective and develop Conservative Quantile Regression (CQR), which combines Conservative Q-Learning and QR-DQN to learn from static data while modeling return distributions. The results show that CQR outperforms baseline schemes and even online RL in the RRM context, demonstrating improved sum-rate and 5th-percentile fairness and highlighting data-efficiency with smaller offline datasets. The approach offers a safer, more robust path to intelligent wireless control for 6G-era networks and can be extended to other optimization tasks such as beamforming and IRS-assisted communications.

Abstract

Reinforcement learning (RL) has proved to have a promising role in future intelligent wireless networks. Online RL has been adopted for radio resource management (RRM), taking over traditional schemes. However, due to its reliance on online interaction with the environment, its role becomes limited in practical, real-world problems where online interaction is not feasible. In addition, traditional RL stands short in front of the uncertainties and risks in real-world stochastic environments. In this manner, we propose an offline and distributional RL scheme for the RRM problem, enabling offline training using a static dataset without any interaction with the environment and considering the sources of uncertainties using the distributions of the return. Simulation results demonstrate that the proposed scheme outperforms conventional resource management models. In addition, it is the only scheme that surpasses online RL with a 10 % gain over online RL.
Paper Structure (15 sections, 15 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 15 sections, 15 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Definition
  4. Reinforcement Learning
  5. Markov Decision Process
  6. Online RL
  7. Offline and Distributional RL
  8. Offline RL
  9. Distributional RL
  10. Conservative Quantile Regression
  11. Experimental Results
  12. Baseline Schemes
  13. Simulation Parameters and Dataset
  14. Simulation Results
  15. Conclusions

Figures (5)

  • Figure 1: The wireless model consists of $N$ APs serving $M$ UEs. The blue lines represent the user association performed at the beginning of each episode, while the red lines represent interference links.
  • Figure 2: An illustrative figure for the proposed CQR algorithm.
  • Figure 3: The convergence of online RL as a function of training episodes compared to the baseline methods. All the results shown are average over $100$ unique test episodes.
  • Figure 4: The convergence of the proposed CQR algorithm as a function of training epochs compared to other offline RL schemes and the baseline methods; the Online method is shown after convergence. All the results shown are average over $100$ unique test episodes.
  • Figure 5: The sum rate, $5$-percentile rate, and Rscore reported for the proposed CQR algorithm compared to other offline RL schemes: (a) to (c) using a dataset of $20 \%$ of the experience of online DQN and (d) to (f) using a dataset of $10 \%$ of the experience of online DQN.