Sample-efficient diffusion-based control of complex nonlinear systems

Hongyi Chen, Jingtao Ding, Jianhai Shu, Xinchun Yu, Xiaojun Liang, Yong Li, Xiao-Ping Zhang

Abstract: Complex nonlinear system control faces challenges in achieving sample-efficient, reliable performance. While diffusion-based methods have demonstrated advantages over classical and reinforcement learning approaches in long-term control performance, they are limited by sample efficiency. This paper presents SEDC (Sample-Efficient Diffusion-based Control), a novel diffusion-based control framework addressing three core challenges: high-dimensional state-action spaces, nonlinear system dynamics, and the gap between non-optimal training data and near-optimal control solutions. Through three innovations - Decoupled State Diffusion, Dual-Mode Decomposition, and Guided Self-finetuning - SEDC achieves 39.5\%-49.4\% better control accuracy than baselines while using only 10\% of the training samples, as validated across three complex nonlinear dynamic systems. Our approach represents a significant advancement in sample-efficient control of complex nonlinear systems. The implementation of the code can be found at https://anonymous.4open.science/r/DIFOCON-C019.

Primary Area: Applications->Chemistry, Physics, and Earth Sciences

Keywords: Complex System, Data-driven Control, Generative Model, Diffusion Model, AI for Science

Submission Number: 9741