Haoran Tang

发表

Predict the Next Location From Trajectory Based on Spatiotemporal Sequence

Ling Liu, Jun Zeng, Sachio Hirokawa, 2019, 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI).

Influence of WAMS data corruption on small disturbance stability and WADC parameter tuning based on DTA

Zhong Chen, Haoran Tang, Tao Zhou, 2017, 2017 IEEE Power and Energy Conference at Illinois (PECI).

Modular Architecture for StarCraft II with Deep Reinforcement Learning pdf

Trevor Darrell, Pieter Abbeel, Huazhe Xu, 2018, AIIDE.

Reinforcement Learning with Deep Energy-Based Policies

Sergey Levine, Pieter Abbeel, Haoran Tang, 2017, ICML.

Predicting the next location: A self‐attention and recurrent neural network model with temporal context

Junhao Wen, Xin He, Jun Zeng, 2020, Trans. Emerg. Telecommun. Technol..

A novel algorithm for fast static security analysis during power grid interaction

Yi Liu, Zhong Chen, Haoran Tang, 2017, 2017 6th International Youth Conference on Energy (IYCE).

Hierarchical Deep Reinforcement Learning Agent with Counter Self-play on Competitive Games

Sergey Levine, Trevor Darrell, Pieter Abbeel, 2018 .

DPR-Geo: A POI Recommendation Model Using Deep Neural Network and Geographical Influence

Junhao Wen, Haoran Tang, Jun Zeng, 2020, ICONIP.

A Deep Learning Model Based on Sparse Matrix for Point-of-Interest Recommendation

Yinghua Li, Jun Zeng, Xin He, 2019, SEKE.

A Next Location Predicting Approach Based on a Recurrent Neural Network and Self-attention

Junhao Wen, Jun Zeng, Xin He, 2019, CollaborateCom.

Towards Informed Exploration for Deep Reinforcement Learning

Haoran Tang, 2019 .

Convolutional neural networks at the interface of physical and digital data

Chao Yang, Romuere Rôdrigues Veloso e Silva, Flávio H. D. Araújo, 2016, 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

Eigen-Analysis Considering Time-Delay and Data-Loss of WAMS and ITS Application to WADC Design Based on Damping Torque Analysis

Zhong Chen, Tao Zhou, Siqi Bu, 2018 .

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

Filip De Turck, Pieter Abbeel, Xi Chen, 2016, NIPS.

Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning? pdf

Sergey Levine, Honglak Lee, Ofir Nachum, 2019, ArXiv.

RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context

Jun Zeng, Haoran Tang, Xin He, 2020, CollaborateCom.