Can offline RL manage radio resources better?
Offline and Distributional Reinforcement Learning for Radio Resource Management
This research paper investigates using offline and distributional Reinforcement Learning (RL) to optimize Radio Resource Management (RRM) in wireless networks. The authors propose a novel algorithm that combines Conservative Q-learning (CQL) and Quantile Regression Deep Q-Network (QR-DQN) to learn optimal resource allocation policies from a static dataset without requiring real-time interaction with the environment. This is particularly relevant to LLM-based multi-agent systems, as training LLMs for complex tasks often relies on large, static datasets. The proposed offline approach provides a more practical and efficient alternative to traditional online RL methods that require continuous, potentially risky, real-time interaction, making it a promising direction for developing robust LLM-based multi-agent applications.