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Full-Text Articles in Engineering
Research On Fire Emergency Evacuation Simulation Based On Cooperative Deep Reinforcement Learning, Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang
Research On Fire Emergency Evacuation Simulation Based On Cooperative Deep Reinforcement Learning, Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang
Journal of System Simulation
Abstract: The fire accident is a major threat to the public safety, in which the high temperature, toxic and harmful gases seriously interfer the selection of the evacuation routes. Deep reinforcement learning is introduced into the research of emergency evacuation simulation, and a cooperative double deep Q network algorithm is proposed for the multi-agent environment. A fire scene model that changes dynamically over time is established to provide the real-time information on the distribution of the dangerous areas for the evacuation. The independent agent neural networks are integrated and the multi-agent unified deep neural network is established to realize the …
A Devs-Based Formal Description Method For Complex Product Behavior Models, Qingquan Lin, Jiaran Yang, Heming Zhang
A Devs-Based Formal Description Method For Complex Product Behavior Models, Qingquan Lin, Jiaran Yang, Heming Zhang
Journal of System Simulation
Abstract: For the online optimization of pedestrian flow control in subway station, an algorithm frame for pedestrian flow control in subway station based on machine learning is designed. The pedestrian flow control process of a subway station during morning rush hour is selected,and the agent-based model is built to simulate the control process. The training data is collected through the multiple runs of the model, which is used as the input of deep reinforcement learning network, and the mature net is obtained through adequate training to provide the optimizing scheduling policy. Linking the actual data with the mature net …
Research On Optimization Of Airport Cargo Business Based On Deep Reinforcement Learning, Hongwei Wang, Peng Yang
Research On Optimization Of Airport Cargo Business Based On Deep Reinforcement Learning, Hongwei Wang, Peng Yang
Journal of System Simulation
Abstract: An intelligent agent technology architecture is adopted to the simulation model development of airport cargo business. Aiming at the optimization of airport cargo resources, a decision support system framework combining deep reinforcement learning (DRL) and airport cargo business simulation model is proposed. The simulated results are applied as the training data of the DRL network, and the DRL is used to optimize operation parameter of the simulation model. The mature system can be run online, which can provide optimized operation order in real time. In order to verify the effectiveness of the architecture, model development and experiments are conducted …
Job Scheduling And Simulation In Cloud Based On Deep Reinforcement Learning, Qirui Li, Xinyi Peng
Job Scheduling And Simulation In Cloud Based On Deep Reinforcement Learning, Qirui Li, Xinyi Peng
Journal of System Simulation
Abstract: To solve the difficulty in job scheduling in the complex and transient multi-user, multi-queue, and multi-data-center cloud computing environment, this paper proposed a job scheduling method based on deep reinforcement learning. A system model of cloud job scheduling and its mathematical model were built, and an optimization goal consisting of transmission time, waiting time, and execution time was obtained. A job scheduling algorithm based on deep reinforcement learning was designed, and its state space, action space, and reward function were given. A simulated cloud job scheduler was designed and developed, and simulated scheduling experiments were conducted on it. The …
A Data-Driven Modeling Method For Game Adversity Agent, Zeng Bi, Fang Xiao, Deshuai Kong, Xiangxiang Song, Zhengxuan Jia, Tingyu Lin
A Data-Driven Modeling Method For Game Adversity Agent, Zeng Bi, Fang Xiao, Deshuai Kong, Xiangxiang Song, Zhengxuan Jia, Tingyu Lin
Journal of System Simulation
Abstract: Aiming at the problems of collaborative modeling of formation behavior and intelligent generation of decision-making in complex confrontation scenarios, based on the serious game to simulate the confrontation scenarios of complex maritime equipment against the air, this paper proposes a data-driven modeling method for game agent and uses a distributed modeling technology of parallel adversarial scenarios and opportunistic decision making technology of smart targets to achieve agent modeling. It provides support for the further exploration of multi-objective collaborative modeling in complex confrontation scenarios. The simulation results show that deep reinforcement learning algorithms can provide a basis for the modeling …