Robustness Analysis of Data-Driven Self-Learning Controllers for IoT Environmental Monitoring Nodes based on Q-learning Approaches-Reference-Cited by-同舟云学术

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Robustness Analysis of Data-Driven Self-Learning Controllers for IoT Environmental Monitoring Nodes based on Q-learning Approaches

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Symposium Series on Computational Intelligence (SSCI)
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Author:

Paterova Tereza¹,Prauzek Michal¹,Konecny Jaromir¹

Affiliation:

1. VSB – Technical University of Ostrava,Department of Cybernetics and Biomedical Engineering,Ostrava,Czech Republic

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10022049/10022014/10022151.pdf?arnumber=10022151

Reference25 articles.

1. Double Q-Learning for Radiation Source Detection

2. Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment

3. Harvesting circuits for triboelectric nanogenerators for wearable applications

4. Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications

5. Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships

Cited by 1 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. Reinforcement Learning-Based Car-Following Control for Autonomous Vehicles with OTFS;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21

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