Sample-efficient diffusion-based control of complex nonlinear systems

• URL: http://arxiv.org/abs/2502.17893v1
• Date: Tue, 25 Feb 2025 06:30:04 GMT
• Title: Sample-efficient diffusion-based control of complex nonlinear systems
• Authors: Hongyi Chen, Jingtao Ding, Jianhai Shu, Xinchun Yu, Xiaojun Liang, Yong Li, Xiao-Ping Zhang,
• Abstract summary: SEDC is a novel diffusion-based control framework addressing high-dimensional state-action spaces, nonlinear system dynamics, and the gap between non-optimal training data and near-optimal control solutions.<n>Our approach represents a significant advancement in sample-efficient control of complex nonlinear systems.
• Score: 12.75120974078924
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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