Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control
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-Lear...
| Authors | Nicolas Helson, Pegah Alizadeh, Anastasios Giovanidis |
|---|---|
| Primary category | cs.NI |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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