Large Language Model-Driven Curriculum Design for Mobile Networks

TL;DR

This paper presents a novel framework that leverages large language models to automate curriculum design for reinforcement learning in mobile networks, enhancing convergence, generalization, and performance in complex 6G scenarios.

Contribution

It introduces an LLM-driven automated curriculum design method for RL in mobile networks, reducing manual effort and improving learning efficiency.

Findings

Improved RL convergence rates in simulated mobile networks

Enhanced generalization to unseen network scenarios

Performance gains in autonomous network coordination tasks

Abstract

This study introduces an innovative framework that employs large language models (LLMs) to automate the design and generation of curricula for reinforcement learning (RL). As mobile networks evolve towards the 6G era, managing their increasing complexity and dynamic nature poses significant challenges. Conventional RL approaches often suffer from slow convergence and poor generalization due to conflicting objectives and the large state and action spaces associated with mobile networks. To address these shortcomings, we introduce curriculum learning, a method that systematically exposes the RL agent to progressively challenging tasks, improving convergence and generalization. However, curriculum design typically requires extensive domain knowledge and manual human effort. Our framework mitigates this by utilizing the generative capabilities of LLMs to automate the curriculum design process, significantly reducing human effort while improving the RL agent's convergence and performance. We deploy our approach within a simulated mobile network environment and demonstrate improved RL convergence rates, generalization to unseen scenarios, and overall performance enhancements. As a case study, we consider autonomous coordination and user association in mobile networks. Our obtained results highlight the potential of combining LLM-based curriculum generation with RL for managing next-generation wireless networks, marking a significant step towards fully autonomous network operations.