Prompt Optimization Through Reinforcement Learning for Generative Language Model Code Synthesis in Multi-Robot Systems

Document Type

Article

Publication Date

6-1-2026

Department

Department of Mechanical and Aerospace Engineering

Abstract

In multi-robot systems (MRS) operating across various applications, real-time task allocation and path planning pose significant challenges, often requiring extensive human intervention under extreme time constraints. This paper introduces a novel framework that leverages Reinforcement Learning (RL) to automate and optimize the code generation process for MRS. Our approach trains an RL agent to dynamically generate optimized prompts for a Large Language Model (LLM). By refining prompt structures, templates, and parameters, the RL agent guides the LLM to produce efficient, feasible, and directly executable code for complex task allocation and path-planning problems. We demonstrate that the generated solutions are both complete and high-performing. The proposed method is evaluated on a sample case of search-and-rescue scenarios, using GPT-4.1 as the LLM, and demonstrates significant performance improvements over previous work that used manual prompt optimization for similar applications. This work represents a significant step towards autonomous multi-robot coordination in time-critical dynamic environments, reducing human workload and improving mission efficacy.

Publication Title

IEEE Robotics and Automation Letters

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