
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.


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rlingua-leveraging-large-language-models-to-improve-reinforcement-learning-sample-efficiency-in-robotic-manipulations


RLingua: Leveraging Large Language Models to Improve Reinforcement Learning Sample Efficiency in Robotic Manipulations
