1 |
F. Yao and L. Jia, "A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks," IEEE Wireless Communications Letters, Vol.8, No.4, pp.1024-1027, Aug. 2019. http://dx.doi.org/10.1109/LWC.2019.2904486
DOI
|
2 |
H. Huang and S. Li, "The Application of Reinforcement Learning in Amazons," in Proceedings of International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, pp.369-372, Jan. 2019. http://dx.doi.org/10.1109/MLBDBI48998.2019.00083
DOI
|
3 |
R. Sutton and A. Barto. Reinforcement Learning, second edition: An Introduction. Cambridge, Massachusetts: Bradford Books. 2018.
|
4 |
H. Schwartz. Multi-Agent Machine Learning: A Reinforcement Approach. Wiley. 2014.
|
5 |
J. Xu et al., "A Multi-Channel Reinforcement Learning Framework for Robotic Mirror Therapy," IEEE Robotics and Automation Letters, Vol.5, No.4, pp.5385-5392, Oct. 2020. http://dx.doi.org/10.1109/LRA.2020.3007408
DOI
|
6 |
S. Mukhopadhyay, O. Tilak and S. Chakrabarti, "Reinforcement Learning Algorithms for Uncertain, Dynamic, Zero-Sum Games," in Proceedings of 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018), Orlando, FL, USA, pp.48-54, Dec. 2018. http://dx.doi.org/10.1109/ICMLA.2018.00015
DOI
|
7 |
G. Sun, G. O. Boateng, et al., "A Reinforcement Learning Framework for Autonomous Cell Activation and Customized Energy-Efficient Resource Allocation in C-RANs," KSII Transactions on Internet and Information Systems, Vol.13, No.8, pp.3821-3841, Aug. 2019. http://dx.doi.org/10.3837/tiis.2019.08.001.
DOI
|
8 |
M. Wang, L. Wang and T. Yue, "An Application of Continuous Deep Reinforcement Learning Approach to Pursuit-Evasion Differential Game," in Proceedings of IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2019), Chengdu, China, pp.1150-1156, March 2019. http://dx.doi.org/10.1109/ITNEC.2019.8729310
DOI
|
9 |
T. Becsi, A. Szabo, et al., "Reinforcement Learning Based Control Design for a Floating Piston Pneumatic Gearbox Actuator," IEEE Access, Vol.8, pp.147295-147312, Aug. 2020. http://dx.doi.org/10.1109/ACCESS.2020.3015576
DOI
|
10 |
S. Choi, et al. "Controller Learning Method of Self-Driving Bicycle Using State-of-the-Art Deep Reinforcement Learning Algorithms," Journal of the Korea Society of Computer and Information, Vol.23, No.10, pp.23-31, Oct. 2018, http://dx.doi.org/10.9708/JKSCI.2018.23.10.023
DOI
|
11 |
D. Bertsekas. Reinforcement Learning and Optimal Control. Athena Scientific. 2019
|
12 |
H. Dong, Z. Ding, and S, Zhang. Deep Reinforcement Learning: Fundamentals, Research and Applications. Springer. 2020.
|
13 |
P. Wang, L. T. Yang, et al., "MMDP: A Mobile-IoT Based Multi-Modal Reinforcement Learning Service Framework," IEEE Transactions on Services Computing, Vol.13, No.10, pp.675-684, Aug. 2020. http://dx.doi.org/10.1109/TSC.2020.2964663
DOI
|
14 |
M. Kang, K. Kim, "Analysis of Reward Functions in Deep Reinforcement Learning for Continuous State Space Control, " Journal of KIISE, Vol.47, No.1, pp.78-87, Jan. 2020. http://dx.doi.org/10.5626/JOK.2020.47.1.78
DOI
|
15 |
Y. Li, W. Zheng and Z. Zheng, "Deep Robust Reinforcement Learning for Practical Algorithmic Trading," IEEE Access, Vol.7, pp.108014-108022, Aug. 2019. http://dx.doi.org/10.1109/ACCESS.2019.2932789
DOI
|
16 |
K. Shao, Y. Zhu, et al., "Cooperative Multi-Agent Deep Reinforcement Learning with Counterfactual Reward," in Proceedings of International Joint Conference on Neural Networks (IJCNN 2020), Glasgow, United Kingdom, pp.1-8, Sep. 2020. http://dx.doi.org/10.1109/IJCNN48605.2020.9207169
DOI
|
17 |
D. Choi and S. Park, "Reinforcement Learning based Single AI trained from Multi-game." Journal of The Korean Society for Computer Game, Vol.32, No.3, pp.59-64, Sep. 2019. http://dx.doi.org/10.21493/kscg.2016.29.2.1
DOI
|
18 |
T. Chu, J. Wang, et al., "Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control," IEEE Transactions on Intelligent Transportation Systems, Vol.21, No.3, pp.1086-1095, March 2020. http://dx.doi.org/10.1109/TITS.2019.2901791
DOI
|
19 |
D. Ha, "Reinforcement Learning for Improving Agent Design," Artificial Life, Vol.25, No.4, pp.352-365, Nov. 2019. http://dx.doi.org/10.1162/artl_a_00301
DOI
|
20 |
S. Woo and Y. Sung, "Dynamic Action Space Handling Method for Reinforcement Learning Models, " Journal of Information Processing Systems, Vol.16, No.5, pp.1223-1230, Oct. 2020. http://dx.doi.org/10.3745/JIPS.02.0146
DOI
|
21 |
A. Zai and B. Brown. Deep Reinforcement Learning in Action. Manning Publications. 2020.
|
22 |
T. Liu, B. Tian, et al., "Parallel reinforcement learning: a framework and case study," IEEE/CAA Journal of Automatica Sinica, Vol.5, No.4, pp.827-835, July 2018. http://dx.doi.org/10.1109/JAS.2018.7511144
DOI
|
23 |
N. Ebell and M. Pruckner, "Coordinated Multi-Agent Reinforcement Learning for Swarm Battery Control," in Proceedings of IEEE Canadian Conference on Electrical & Computer Engineering (CCECE 2018), Quebec City, QC, Canada, pp.1-4, May 2018. http://dx.doi.org/10.1109/CCECE.2018.8447851
DOI
|
24 |
M. A. Abd-Elmagid, H. S. Dhillon and N. Pappas, "A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems," IEEE Transactions on Communications, Vol.68, No.8, pp.4747-4760, Aug. 2020. http://dx.doi.org/10.1109/TCOMM.2020.2991992
DOI
|
25 |
W. Tang, B. Li, et al., "An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning," IEEE Transactions on Information Forensics and Security, Vol.16, pp.952-967, 2021. http://dx.doi.org/10.1109/TIFS.2020.3025438
DOI
|