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Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control

Nicolas Helson, Pegah Alizadeh, Anastasios Giovanidis

TL;DR

Conservative Q-Learning consistently produces more robust policies across different sources of stochasticity, making it a reliable default choice in lifecycle-driven AI management frameworks, and sequence-based methods remain competitive and can outperform Bellman-based approaches when sufficient high-return trajectories are available.

Abstract

Offline Reinforcement Learning (RL) is a promising approach for next-generation wireless networks, where online exploration is unsafe and large amounts of operational data can be reused across the model lifecycle. However, the behavior of offline RL algorithms under genuinely stochastic dynamics -- inherent to wireless systems due to fading, noise, and traffic mobility -- remains insufficiently understood. We address this gap by evaluating Bellman-based (Conservative Q-Learning), sequence-based (Decision Transformers), and hybrid (Critic-Guided Decision Transformers) offline RL methods in an open-access stochastic telecom environment (mobile-env). Our results show that Conservative Q-Learning consistently produces more robust policies across different sources of stochasticity, making it a reliable default choice in lifecycle-driven AI management frameworks. Sequence-based methods remain competitive and can outperform Bellman-based approaches when sufficient high-return trajectories are available. These findings provide practical guidance for offline RL algorithm selection in AI-driven network control pipelines, such as O-RAN and future 6G functions, where robustness and data availability are key operational constraints.

Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control

TL;DR

Conservative Q-Learning consistently produces more robust policies across different sources of stochasticity, making it a reliable default choice in lifecycle-driven AI management frameworks, and sequence-based methods remain competitive and can outperform Bellman-based approaches when sufficient high-return trajectories are available.

Abstract

Offline Reinforcement Learning (RL) is a promising approach for next-generation wireless networks, where online exploration is unsafe and large amounts of operational data can be reused across the model lifecycle. However, the behavior of offline RL algorithms under genuinely stochastic dynamics -- inherent to wireless systems due to fading, noise, and traffic mobility -- remains insufficiently understood. We address this gap by evaluating Bellman-based (Conservative Q-Learning), sequence-based (Decision Transformers), and hybrid (Critic-Guided Decision Transformers) offline RL methods in an open-access stochastic telecom environment (mobile-env). Our results show that Conservative Q-Learning consistently produces more robust policies across different sources of stochasticity, making it a reliable default choice in lifecycle-driven AI management frameworks. Sequence-based methods remain competitive and can outperform Bellman-based approaches when sufficient high-return trajectories are available. These findings provide practical guidance for offline RL algorithm selection in AI-driven network control pipelines, such as O-RAN and future 6G functions, where robustness and data availability are key operational constraints.
Paper Structure (25 sections, 1 theorem, 19 equations, 6 figures, 9 tables)

This paper contains 25 sections, 1 theorem, 19 equations, 6 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Studied algorithms
  4. Telecom Environment
  5. Experiments
  6. How is the telecom-agents' performance perturbed by user mobility (state transition stochasticity)?
  7. How does dataset quality impact performance of the telecom-agents under user mobility? (epistemic uncertainty)
  8. How is the telecom-agents' performance perturbed by channel fading (reward stochasticity)?
  9. Conclusion
  10. Additional experiments on LunarLander
  11. Environment and Stochasticity Setup
  12. Experiments
  13. Modified Environment mobile-env
  14. Limited Mobility
  15. Modifications of mobile-env
  16. ...and 10 more sections

Key Result

Theorem 1

The expected reward under fading is always less than or equal to the baseline reward:

Figures (6)

  • Figure 1: Mobile-env environment illustration.
  • Figure 2: Low-mobility variant of the Random Waypoint model.
  • Figure 3: Return distributions of the training trajectories for mobile-env under different mobility stochasticity settings.
  • Figure 4: Target return plots for mobile-env in the limited mobility stochasticity setting with the medium/expert dataset.
  • Figure 5: Target return plots for mobile-env in the high mobility stochasticity setting with the medium/expert dataset.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Theorem 1
  • proof