LLM-guided population-based reinforcement learning: A scalable methodology for adaptive hyperparameter optimization.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Population-Based Training (PBT) uses fixed rules for hyperparameter optimization, limiting flexibility in reinforcement learning (RL) tasks.
• Existing LLM-assisted frameworks often operate outside the core training loop, missing opportunities for dynamic adaptation.

Purpose of the Study:

• Introduce LLM-Guided Population-Based Reinforcement Learning (LPBRL) for dynamic, adaptive hyperparameter optimization in RL.
• Integrate Large Language Models (LLMs) directly into the population evolution process for context-aware decision-making.

Main Methods:

• LPBRL employs a six-phase cycle where LLMs analyze real-time performance metrics from parallel workers.
• LLM reasoning guides adaptive population-update recommendations, replacing static mutation and selection rules.
• The methodology ensures reproducible implementation with structured prompts and compatibility with RL algorithms like PPO, SAC, and TD3.

Main Results:

• LPBRL demonstrated significant improvements over conventional PBT in CartPole-v1 evaluations.
• Best- and average-reward convergence improved by 62.5% and 68.2%, respectively.
• Accelerated convergence and enhanced training stability were observed across discrete and continuous-control RL settings.

Conclusions:

• LLM reasoning can effectively manage RL optimization decisions within the population-based training loop.
• LPBRL offers a scalable approach for large-scale training workflows requiring adaptive hyperparameter control.
• The integration of LLMs enhances RL training efficiency and stability.