Reinforcement Learning Enhanced LLM Agents for Complex Vehicle Routing Problems
The experimental results demonstrate that RLEA outperforms the previous state-of-the-ar method, achieving a 16.67% higher success rate while significantly reducing runtime errors, and validate that integrating reinforcement learning with LLM-based reasoning is highly effective for automated optimization modeling.
Yi Chen, Zi-Pei Yu, Jia-Hai Wang et al.
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