Journal of System Simulation
Abstract
Abstract: Reinforcement learning simulation platform can be an interactive and training environment for reinforcement learning. In order to make the simulation platform compatible with the multi-agent reinforcement learning algorithms and meet the needs of simulation in military field, the similar processes in multi-agent reinforcement learning algorithms are refined and a unified interface is designed to embed and verify different types of deep reinforcement learning algorithms on the simulation platform and to optimize the back-end service of the simulation platform to accelerate the training process of the algorithm model. The experimental results show that, by unifing the interface, the simulation platform can be compatible with many different types of multi-agent reinforcement learning algorithms, and the algorithm training efficiency can be significantly improved after the back-end service framework reconstruction and parameter quantization.
Recommended Citation
Cheng, Cheng; Chen, Zhijie; Guo, Ziming; and Li, Ni
(2023)
"Research and Development of Simulation Training Platform for Multi-agent Collaborative Decision-making,"
Journal of System Simulation: Vol. 35:
Iss.
12, Article 15.
DOI: 10.16182/j.issn1004731x.joss.23-FZ0821
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss12/15
First Page
2669
Last Page
2679
CLC
TP391.9
Recommended Citation
Cheng Cheng, Chen Zhijie, Guo Ziming, et al. Research and Development of Simulation Training Platform for Multi-agent Collaborative Decision-making[J]. Journal of System Simulation, 2023, 35(12): 2669-2679.
DOI
10.16182/j.issn1004731x.joss.23-FZ0821
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