用图像和动作数据实时估计系统状态并控制，无需真实状态信息。
State Estimation and Control of Dynamic Systems from High-Dimensional Image Data
- CNN+GRU融合提取图像序列中的时空特征
- 在无真实状态条件下实现高精度实时控制
- 提供可量化的状态估计质量评估方法
精确的状态估计对动态系统最优策略设计至关重要，但真实状态往往难以获取，影响策略学习。本文提出一种新型神经架构，结合卷积神经网络（CNN）进行空间特征提取与门控循环单元（GRU）进行时序建模，从图像序列及对应动作中学习有效状态表示。利用这些学习到的状态表示训练深度Q网络（DQN）强化学习智能体。实验表明，该方法可在无真实状态访问的情况下实现实时、准确的状态估计与控制。此外，本文还提供了定量评估方法，用于分析学习状态的准确性及其对策略性能与控制稳定性的影响。
原文摘要 · Abstract (English)
Accurate state estimation is critical for optimal policy design in dynamic systems. However, obtaining true system states is often impractical or infeasible, complicating the policy learning process. This paper introduces a novel neural architecture that integrates spatial feature extraction using convolutional neural networks (CNNs) and temporal modeling through gated recurrent units (GRUs), enabling effective state representation from sequences of images and corresponding actions. These learned state representations are used to train a reinforcement learning agent with a Deep Q-Network (DQN). Experimental results demonstrate that our proposed approach enables real-time, accurate estimation and control without direct access to ground-truth states. Additionally, we provide a quantitative evaluation methodology for assessing the accuracy of the learned states, highlighting their impact on policy performance and control stability.
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