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Tianheng Song
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2020 – today
- 2022
- [j9]Yi Liang, Zan Sun
, Tianheng Song, Qiang Chou, Wei Fan, Jianping Fan, Yong Rui, Qiping Zhou, Jessie Bai, Chun Yang, Peng Bai:
Lenovo Schedules Laptop Manufacturing Using Deep Reinforcement Learning. INFORMS J. Appl. Anal. 52(1): 56-68 (2022) - [j8]Tianheng Song
, Dazi Li
, Xin Xu
:
Online Sparse Temporal Difference Learning Based on Nested Optimization and Regularized Dual Averaging. IEEE Trans. Syst. Man Cybern. Syst. 52(4): 2042-2052 (2022) - 2021
- [j7]Tianheng Song
, Dazi Li
, Weimin Yang, Kotaro Hirasawa:
Recursive Least-Squares Temporal Difference With Gradient Correction. IEEE Trans. Cybern. 51(8): 4251-4264 (2021) - [j6]Luntong Li
, Dazi Li
, Tianheng Song
, Xin Xu
:
Actor-Critic Learning Control With Regularization and Feature Selection in Policy Gradient Estimation. IEEE Trans. Neural Networks Learn. Syst. 32(3): 1217-1227 (2021) - 2020
- [j5]Tianheng Song
, Dazi Li
, Qibing Jin
, Kotaro Hirasawa:
Sparse Proximal Reinforcement Learning via Nested Optimization. IEEE Trans. Syst. Man Cybern. Syst. 50(11): 4020-4032 (2020)
2010 – 2019
- 2019
- [j4]Tianheng Song, Dazi Li
, Zhiyin Liu, Weimin Yang:
Online ADMM-Based Extreme Learning Machine for Sparse Supervised Learning. IEEE Access 7: 64533-64544 (2019) - 2018
- [j3]Dazi Li
, Yuting Wang, Tianheng Song, Qibing Jin:
An Adaptive Policy Evaluation Network Based on Recursive Least Squares Temporal Difference With Gradient Correction. IEEE Access 6: 7515-7525 (2018) - [j2]Luntong Li
, Dazi Li
, Tianheng Song
, Xin Xu
:
Actor-Critic Learning Control Based on ℓ2-Regularized Temporal-Difference Prediction With Gradient Correction. IEEE Trans. Neural Networks Learn. Syst. 29(12): 5899-5909 (2018) - 2017
- [c1]Luntong Li, Dazi Li, Tianheng Song:
Sustainable ℓ2-regularized actor-critic based on recursive least-squares temporal difference learning. SMC 2017: 1886-1891 - 2016
- [j1]Tianheng Song, Dazi Li, Liulin Cao, Kotaro Hirasawa:
Kernel-Based Least Squares Temporal Difference With Gradient Correction. IEEE Trans. Neural Networks Learn. Syst. 27(4): 771-782 (2016)
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