Daily Schedule Recommendation in Urban Life Based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Daily Schedule Recommendation in Urban Life Based on Deep Reinforcement Learning

Published:2024 Issue: Volume: Page:1-10
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Liu Jia¹^ORCID,Zhai Donghai²^ORCID,Huang Wei²^ORCID,Ji Shenggong³^ORCID,Zhang Junbo⁴^ORCID,Li Tianrui²^ORCID

Affiliation:

1. School of Computer and Software Engineering, Xihua University, Chengdu, China

2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

3. Tencent Inc, Shenzhen, China

4. JD Intelligent Cities Research, China and JD iCity, JD Tech, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10413499.pdf?arnumber=10413499

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FedDAF: Federated deep attention fusion for dangerous driving behavior detection;Information Fusion;2024-12

2. Q-ADER: An Effective Q-Learning for Recommendation With Diminishing Action Space;IEEE Transactions on Neural Networks and Learning Systems;2024

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