  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.
