Application of Reinforcement Learning in Complex Environmental Decision-making Problems

Authors

  • Chenchen Zhao

DOI:

https://doi.org/10.54097/a1nnjq52

Keywords:

Reinforcement learning, Complex environment decision-making, Multi-intelligence reinforcement learning, Intelligent transportation system

Abstract

This paper provides an in-depth discussion on the application of reinforcement learning in decision-making problems in complex environments, focusing on the challenges it faces such as data sparsity and the balance between exploration and utilization, and proposes corresponding solutions. The study realizes an integrated process from data collection to decision support by developing a decision support system for sustainable enterprise management based on big data. Meanwhile, the exploration strategies and optimization algorithms in reinforcement learning are used to improve decision-making efficiency and accuracy. An application case in an intelligent transportation system demonstrates the potential of the multi-intelligence reinforcement learning framework in vehicle collaboration and optimization of traffic flow, effectively alleviating traffic congestion. Experiments show that the system significantly improves decision-making efficiency and accuracy, verifying the effectiveness of reinforcement learning in decision-making in complex environments.

References

[1] Li Hui,Qi Yuming. A deep reinforcement learning-based path planning method for robots in complex environments[J]. Computer Application Research, 2020,37(S1):129-131.

[2] Yin Chenkun,Ji Hongxuan,Zhang Yanxin. Autonomous decision making based on heterogeneous policy hierarchical reinforcement learning for search and rescue robots in complex interactive scenarios[J]. Journal of Beijing Institute of Technology, 2023,49(04):403-414.

[3] ZHANG Wang,SHAO Xuehui,TANG Huilong,et al. A reinforcement learning interference decision-making method for exploring rate adaptive settings[J/OL]. Journal of Military Engineering,1-10[2024-09-03]

.http://kns.cnki.net/kcms/detail/11.2176.tj.20240827.1543.004.html.

[4] Luo Qing,Li Zhijun,Lv Tiansheng. Multi-intelligent reinforcement learning in complex environments [J]. Journal of Shanghai Jiao Tong University, 2002, (03):302-305.

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Published

26-12-2024

Issue

Section

Articles

How to Cite

Zhao, C. (2024). Application of Reinforcement Learning in Complex Environmental Decision-making Problems. Journal of Computing and Electronic Information Management, 15(3), 104-108. https://doi.org/10.54097/a1nnjq52