Bi-Dueling DQN Enhanced Two-Stage Scheduling for Augmented Surveillance in Smart EMS

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Safety production surveillance is of great significance to industrial operation management. While augmented intelligence of things is demonstrating tremendous potential in industrial applications, the analyzed information offers lots of benefits to the higher-level planning in the enterprise management systems, to further improve the operational efficiency. In this study, a video surveillance system with augmented intelligence of things is considered as a promising solution to enhance the operational efficiency for enterprises. However, the challenge is to process the surveillance video streams as soon as possible without ignoring any emergencies. This issue can be formulated as a two-stage scheduling problem, which is an NP-hard problem that can be integrated with higher-level enterprise systems for operational efficiency improvement. An improved Deep Q-Network (DQN) model with a newly designed prioritized replay scheme, named Bi-Dueling DQN with Prioritized Replay (Bi-DPR), is proposed to solve this two-stage scheduling problem in smart enterprise management system. A dense reward function based on a concrete state representation is designed to tackle the sparse reward challenge and to speed up the convergence in actual large-scale task scheduling process. A prioritized replay scheme is then developed to improve the sampling efficiency, so as to effectively reduce the training time in Deep Reinforcement Learning (DRL) for the optimal two-stage scheduling. The experiment results demonstrated that the proposed approach is able to provide an efficient scheduling policy to resolve the two-stage scheduling problem, while at the same time offer insight information to improve the performance of higher-level smart enterprise management system.