Journal of System Simulation
Abstract
Abstract: Based on the perfect performance of deep reinforcement learning (DRL) in strategy optimization, this paper proposes a strategy optimization method of action taking the multi-dimension projection action as the main research object. The method combines the simulation experiment method with the DRL method. After analyzing the current situation of strategy optimization research, the deep learning framework is selected according to the research problems, and a DRL multi-dimension projection strategy model based on the asynchronous advantage actor-critic (A3C) algorithm is constructed. Through simulation experiments, the interactive learning between the DRL model and the simulation of "out of the loop" is realized, and the optimized multi-dimension projection strategy is obtained. Finally, the effectiveness of the cooperative optimization strategy between the DRL framework and the simulation experiment is verified.
Recommended Citation
An, Jing; Si, Guangya; and Zhang, Lei
(2024)
"Strategy Optimization Method of Multi-dimension Projection Based on Deep Reinforcement Learning,"
Journal of System Simulation: Vol. 36:
Iss.
1, Article 3.
DOI: 10.16182/j.issn1004731x.joss.22-0886
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss1/3
First Page
39
Last Page
49
CLC
TP391.9
Recommended Citation
An Jing, Si Guangya, Zhang Lei. Strategy Optimization Method of Multi-dimension Projection Based on Deep Reinforcement Learning[J]. Journal of System Simulation, 2024, 36(1): 39-49.
DOI
10.16182/j.issn1004731x.joss.22-0886
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