Discovering Interpretable Programmatic Policies via Multimodal LLM-assisted Evolutionary Search

Abstract: Interpretability and high performance are essential goals in designing control policies, particularly for safety-critical tasks. Deep reinforcement learning has greatly enhanced performance, yet its inherent lack of interpretability often undermines trust and hinders real-world deployment. This work addresses these dual challenges by introducing a novel approach for programmatic policy discovery, called Multimodal LLM-assisted Evolutionary Search (MLES). MLES utilizes multimodal LLMs as policy generators, combining them with evolutionary mechanisms for automatic policy optimization. It integrates visual feedback-driven behavior analysis within the policy generation process to identify failure patterns and facilitate targeted improvements, enhancing the efficiency of policy discovery and producing adaptable, human-aligned policies. Experimental results show that MLES achieves policy discovery capabilities and efficiency comparable to Proximal Policy Optimization (PPO) across two control tasks, while offering transparent control logic and traceable design processes. This paradigm overcomes the limitations of predefined domain-specific languages, facilitates knowledge transfer and reuse, and is scalable across various control tasks. MLES shows promise as a leading approach for the next generation of interpretable control policy discovery.