Sample-efficient diffusion-based control of complex nonlinear systems

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 atthis https URL.

@article{chen2025_2502.17893,
  title={ Sample-efficient diffusion-based control of complex nonlinear systems },
  author={ Hongyi Chen and Jingtao Ding and Jianhai Shu and Xinchun Yu and Xiaojun Liang and Yong Li and Xiao-Ping Zhang },
  journal={arXiv preprint arXiv:2502.17893},
  year={ 2025 }
}