Remote state estimation of large-scale distributed dynamic processes plays an
important role in Industry 4.0 applications. In this paper, by leveraging the
theoretical results of structural properties of optimal scheduling policies, we
develop a structure-enhanced deep reinforcement learning (DRL) framework for
optimal scheduling of a multi-sensor remote estimation system to achieve the
minimum overall estimation mean-square error (MSE). In particular, we propose a
structure-enhanced action selection method, which tends to select actions that
obey the policy structure. This explores the action space more effectively and
enhances the learning efficiency of DRL agents. Furthermore, we introduce a
structure-enhanced loss function to add penalty to actions that do not follow
the policy structure. The new loss function guides the DRL to converge to the
optimal policy structure quickly. Our numerical results show that the proposed
structure-enhanced DRL algorithms can save the training time by 50% and reduce
the remote estimation MSE by 10% to 25%, when compared to benchmark DRL
algorithms.

本文研发了一种结构增强的深度强化学习算法用于最小化远程估计均方误差，实现在多传感器远程估计系统的最优调度，该算法可以比基准算法节省 50％的训练时间并将远程估计均方误差降低 10％至 25％。

结构增强的深度强化学习在最优传输调度中的应用

Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling

In terms of the operation of microgrids, optimal scheduling is a vital issue
that must be taken into account. In this regard, this paper proposes an
effective framework for optimal scheduling of renewable microgrids considering
energy storage devices, wind turbines, micro turbines. Due to the nonlinearity
and complexity of operation problems in microgrids, it is vital to use an
accurate and robust optimization technique to efficiently solve this problem.
To this end, in the proposed framework, the teacher learning-based optimization
is utilized to efficiently solve the scheduling problem in the system.
Moreover, a deep learning model based on bidirectional long short-term memory
is proposed to address the short-term wind power forecasting problem. The
feasibility and performance of the proposed framework as well as the effect of
wind power forecasting on the operation efficiency are examined using IEEE
33-bus test system. Also, the Australian Wool north wind site data is utilized
as a real-world dataset to evaluate the performance of the forecasting model.
Results show the effective and efficient performance of the proposed framework
in the optimal scheduling of microgrids.

本文提出基于教师学习优化的框架和基于双向长短期记忆的深度学习模型的组合，以实现考虑能量储存装置、风力涡轮机、微型涡轮机的可再生微网的优化调度。结果表明，本文提出的框架在微电网的优化调度方面是有效的和高效的。