Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications-Reference-Cited by-同舟云学术

Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications

Author:

Hieu Nguyen Quang¹^ORCID,Hoang Dinh Thai¹^ORCID,Niyato Dusit²^ORCID,Wang Ping³^ORCID,Kim Dong In⁴^ORCID,Yuen Chau⁵^ORCID

Affiliation:

1. School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, Australia

2. School of Computer Science and Engineering, Nanyang Technological University, Singapore

3. Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada

4. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea

5. Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design, Singapore

Funder

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

Reference41 articles.

1. Probabilistic policy reuse in a reinforcement learning agent;fernández;Proc Int'l Joint Conf Autonomous Agents and Multiagent Systems (AAMAS),2006

2. Programming chirp parameters in Ti radar devices;dham,2017

Cited by 16 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

3. Partially Observable Model-Based Learning FOR ISAC Resource Allocation;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14