State-Action Inpainting Diffuser for Continuous Control with Delay

• URL: http://arxiv.org/abs/2603.01553v1
• Date: Mon, 02 Mar 2026 07:28:27 GMT
• Title: State-Action Inpainting Diffuser for Continuous Control with Delay
• Authors: Dongqi Han, Wei Wang, Enze Zhang, Dongsheng Li,
• Abstract summary: State-Action Inpainting Diffuser (SAID) is a framework that integrates the inductive bias of dynamics learning with the direct decision-making capability of policy optimization.<n>Our study suggests a new methodology to advance the field of continuous control and reinforcement learning with delay.
• Score: 28.10905055038984
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Signal delay poses a fundamental challenge in continuous control and reinforcement learning (RL) by introducing a temporal gap between interaction and perception. Current solutions have largely evolved along two distinct paradigms: model-free approaches which utilize state augmentation to preserve Markovian properties, and model-based methods which focus on inferring latent beliefs via dynamics modeling. In this paper, we bridge these perspectives by introducing State-Action Inpainting Diffuser (SAID), a framework that integrates the inductive bias of dynamics learning with the direct decision-making capability of policy optimization. By formulating the problem as a joint sequence inpainting task, SAID implicitly captures environmental dynamics while directly generating consistent plans, effectively operating at the intersection of model-based and model-free paradigms. Crucially, this generative formulation allows SAID to be seamlessly applied to both online and offline RL. Extensive experiments on delayed continuous control benchmarks demonstrate that SAID achieves state-of-the-art and robust performance. Our study suggests a new methodology to advance the field of RL with delay.

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