Journal of System Simulation
Abstract
Abstract: Joint scheduling of heterogeneous TT&C resources as research object, a deep Q network (DQN) algorithm based on reinforcement learning is proposed. The characteristics of the joint scheduling problem of heterogeneous TT&C resources being fully analyzied and mathematical language being used to describe the constraints affecting the solution, a resource joint scheduling model is established. From the perspective of applying reinforcement learning, two neural networks with the same structure and the action selection strategies based onεgreedy algorithm are respectively designed after Markov decision process description, and DQN solution framework is established. The simulation results show that DQN-based heterogeneous TT&C resources scheduling method can identify a TT&C scheduling scheme with better scheduling revenue than the genetic algorithm.
Recommended Citation
Xue, Naiyang; Ding, Dan; Jia, Yutong; Wang, Zhiqiang; and Liu, Yuan
(2023)
"DQN-based Joint Scheduling Method of Heterogeneous TT&C Resources,"
Journal of System Simulation: Vol. 35:
Iss.
2, Article 18.
DOI: 10.16182/j.issn1004731x.joss.21-0879
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss2/18
First Page
423
Revised Date
2021-10-11
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-0879
Last Page
434
CLC
TP273+.1
Recommended Citation
Naiyang Xue, Dan Ding, Yutong Jia, Zhiqiang Wang, Yuan Liu. DQN-based Joint Scheduling Method of Heterogeneous TT&C Resources[J]. Journal of System Simulation, 2023, 35(2): 423-434.
DOI
10.16182/j.issn1004731x.joss.21-0879
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