RLingua: Leveraging Large Language Models to Improve Reinforcement Learning Sample Efficiency in Robotic Manipulations
The author proposes RLingua, a framework that utilizes large language models to reduce the sample complexity of reinforcement learning in robotic manipulations. By generating rule-based controllers from LLMs and refining them through RL, RLingua significantly enhances the efficiency of RL processes.