余超(中山大學計算機學院副教授)

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余超，男，博士，中山大學計算機學院副教授，國家“香江學者”。2007年本科畢業於華中科技大學電信系，獲通信工程學士學位，2013年博士畢業於澳大利亞伍倫貢大學計算機系，獲計算機博士學位。

主要從事強化學習、智慧型醫療、智慧型機器人、智慧型集群等方面的研究工作。先後在IEEE TNNLS, IEEE TCB，IEEE ITS, IEEE TVT，ACM T等國際期刊和ICML、NeurIPS、AAAI、IJCAI、AAMAS上發表學術論文100餘篇，獲最佳論文獎3次。主持科研項目20餘項。

基本介紹

  • 中文名：余超
  • 畢業院校：澳大利亞伍倫貢大學
  • 學位/學歷：博士
  • 職業：高校教師
  • 專業方向：多智慧型體系統理論等
  • 任職院校：中山大學
研究領域,海外經歷,獲獎榮譽,科研項目,學術兼職,教授課程,代表論著,

研究領域

（1）智慧型體與多智慧型體系統理論：強化學習、多智慧型體強化學習、博弈論、計算經濟學、社會選擇理論
（2）智慧型體與多智慧型體系統套用：自動駕駛與智慧型網聯汽車、機器人與多機器人系統、智慧型電網、量化交易
（3）基於強化學習的機器人行為控制：深度強化學習、人機互動、遷移學習、虛實混合（數字孿生）、模型學習
（4）基於強化學習的博弈對抗技術：智慧型集群、非完全信息博弈（實時戰略遊戲、棋牌）
（5）基於強化學習的智慧型醫療：慢性病治療、重症室（膿毒症、呼吸機）決策、健康管理、運動康復

海外經歷

  • 2018.1-2019.6， 香港浸會大學/計算機系，研究員
  • 2010.9-2013.12，澳大利亞伍倫貢大學/計算機與軟體工程系，博士

獲獎榮譽

  • 國家“香江學者”
  • IEEE Conference on Games機器人遷移強化學習挑戰賽冠軍
  • 大連市高層次創新人才
  • 大連理工“星海學者”
  • 遼寧省自然科學學術成果獎（論文類）三等獎, 2018
  • 遼寧省自然科學學術成果獎（論文類）二等獎, 2016, 2017
  • 大連市自然科學優秀學術論文獎二等獎, 2017
  • 大連市自然科學優秀學術論文獎一等獎, 2016
  • 大連理工大學教學質量優良獎, 2016
  • 大連理工大學“優秀黨員“, 2016
  • 大連理工大學“優秀工會工作積極分子”, 2016，2017

科研項目

20餘項

學術兼職

Editor
· Special Section on Frontiers in Agent-based Technology, IEICE Trans. Information and Systems.
· Special Issue on Agent-Based Modelling for Complex Systems, J. Systems Science and Engineering.
Organisers
· Workshop on Reinforcement Learning at DAI'19
· Workshop on Methods and Applications of Reinforcement Learning @ PRICAI2018, Nanjing, China, August, 2018.
· Special Track on Reinforcement Learning @ PRICAI2018, Nanjing, China, August, 2018.
· IEEE ICA2017, 2nd IEEE International Conference on Agents (2017 IEEE ICA), Beijing, China, July 6-9, 2017.
· IEEE ICA2016, IEEE International Conference on Agents (2016 IEEE ICA), Metsue, Japan, September 28-30, 2016.
· ACAN 2016, The 8th International Workshop on Agent-based Complex Automated Negotiations (ACAN2016@AAMAS2016), Singapore, May 4, 2016.
· MATCSD2015, Multi-Agent Technologies for Complex Systems Development: Challenges and Solutions, Dalian University of Technology, China, September 17-18, 2015.
PC
IJCAI2019，AAMAS2019，AAAI2019，AAMAS2018，PRICAI2018，AAMAS2017

教授課程

1. 《強化學習》
2. 《多智慧型體系統》
3. 《推理與學習》
4. 《彙編語言》

代表論著

期刊論文
  1. Chao Yu, JIming Liu and Shamim Nemati.Reinforcement Learning in Healthcare: A SurveyarXiv preprint arXiv:1908.08796, 2019.
  2. Chao Yu, Xin Wang, Xin Xu, et al. Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs.IEEE Transactions Intelligent Transportation Systems, doi: 10.1109/TITS.2019.2893683, 2019. (IF:4.051)
  3. Chao Yu, Jiming Liu and Hongyi Zhao. Inverse Reinforcement Learning for Intelligent Mechanical Ventilation and Sedative Dosing in Intensive Care Units. BMC Medical Informatics and Decision Making, 2019. (IF:2.134)
  4. Chao Yu, Yinzhao Dong and Jiming Liu, and Guoqi Ren. Incorporating Causal Factors into Reinforcement Learning for Dynamic Treatment Regimes in HIV. BMC Medical Informatics and Decision Making, 2019. (IF:2.134)
  5. Bingcai Chen, Chao Yu, Qishaui Diao, Rui Liu and Yuliang Wang. Social or Individual Learning? An Aggregated Solution for Coordination in Multiagent Systems. Journal of Systems Science and Systems Engineering, 27 (2), 180-200 (IF:0.766)
  6. Bingcai Chen, Xin Tao, Manrou Yang, Chao Yu, Weimin Pan, Victor C. M. Leung: A Saliency Map Fusion Method Based on Weighted DS Evidence Theory. IEEE Access 6: 27346-27355 (2018)
  7. Bingcai Chen, Zhenguo Gao, Manrou Yang, Qian Ning, Chao Yu, Weimin Pan, Mei Nian, Dongmei Xie: Packet Multicast in Cognitive Radio Ad Hoc Networks: A Method Based on Random Network Coding. IEEE Access 6: 8768-8781 (2018)
  8. Fuxin Zhang, Guozhen Tan, Chao Yu. Fair Transmission Rate Adjustment in Cooperative Vehicle Safety Systems based on Multi-Agent Model Predictive Control.IEEE Transactions on Vehicular Technology.66(7): 6115-6129, 2017. (IF:4.432)
  9. J Hao, J Sun, G Chen, Z Wang, Chao Yu, Z Ming, Efficient and Robust Emergence of Norms through Heuristic Collective Learning. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 12 (4), 23, 2017 (IF:1.216)
  10. Chao Yu, Guozhen Tan, Hongtao Lv, Zhen Wang, Jun Meng, Jianye Hao and Fenghui Ren. Modelling adaptive learning behaviours for consensus formation in human societies. Scientific Reports, 6, 2016. (IF:4.122)
  11. Chao Yu, Minjie Zhang, Fenghui Ren, and Guozhen Tan. Emotional Multiagent Reinforcement Learning in Spatial Social Dilemmas,IEEE Transactions on Neural Networks and Learning Systems. 26(12), 3083-3096, 2015. (IF:11.683)
  12. Chao Yu, Minjie Zhang, Fenghui Ren, and Guozhen Tan. Multiagent Learning of Coordination in Loosely Coupled Multiagent Systems,IEEE Transactions on Cybernetics. 45(12), 2853-2867, 2015. (IF:10.387)
  13. Chao Yu, Minjie Zhang and Fenghui Ren and Guozhen Tan. Emergence of Social Norms through Collective Learning in Networked Multiagent Systems,IEEE Transactions on Cybernetics, 44(12): 2342-2355, 2014. (IF:10.387)
  14. Chao Yu, Minjie Zhang and Fenghui Ren. Coordinated Learning by Exploiting Sparse Interaction in Multiagent Systems, Concurrency and Computation: Practice and Experience, 26(1): 51-70., 2014. (IF:1.114)
  15. Zhen Wang, Chao Yu, Guanghai Cui, Yapeng Li, Mingchu Li, Evolution of Cooperation in Spatial Iterated Prisoner’s Dilemma Games under Localized Extremal Dynamics. Physica A: Statistical Mechanics and its Applications, 444:566-575, 2016. (IF:2.132)
  16. Jiankang Ren, Zichuan Xu, Chao Yu, Chi Lin, Guowei Wu, Guozhen Tan: Execution allowance based fixed priority scheduling for probabilistic real-time systems. Journal of Systems and Software 152: 120-133 (2019)
  17. Bingcai Chen, Zhongru Ren, Chao Yu, Iftikhar Hussain, Jintao Liu: Adversarial Examples for CNN-Based Malware Detectors. IEEE Access 7: 54360-54371 (2019)
會議論文
  1. Qian Lin, Bo Tang, Zifan Wu, Chao Yu*, et al. Safe Offline Reinforcement Learning with Real-Time Budget Constraints, ICML2023
  2. Wenxuan Zhu, Chao Yu*, Qiang Zhang. Causal Deep Reinforcement Learning using Observational Data, IJCAI2023
  3. Chao Yu, Hierarchical Mean-Field Deep Reinforcement Learning for Large-Scale Multiagent Systems, AAAI2023
  4. Zifan Wu, Chao Yu*, et al. Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning, AAAI2023
  5. Pei Xu, Junge Zhang, Qiyue Yin, Chao Yu, Yaodong Yang, Kaiqi Huang. Subspace-Aware Exploration for Sparse-Reward Multi-Agent Tasks, AAAI2023
  6. Yucong Zhang, Chao Yu*, et al. EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning, AAMAS2023
  7. Zongkai Liu, Chao Yu*, et al. A Unified Diversity Measure for Multiagent Reinforcement Learning, NeurlPS2022.
  8. Zifan Wu, Chao Yu*, et al. Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning, NeurlPS2022.
  9. Mu Jin, Zhihao Ma, Kebin Jin, Hankui Zhuo, Chen Chen, Chao Yu, SORL: Automatic Symbolic Option Discovery for Facilitating Deep Reinforcement Learning, AAAI2022
  10. Zifan Wu, Chao Yu*, et al. Coordinated Proximal Policy Optimization, NeurlPS2021.
  11. Chao Yu, et al. Decomposed Deep Reinforcement Learning for Robotic Control, AAMAS2020.
  12. Chao Yu, et al. Interactive RL via Online Human Demonstrations, AAMAS2020.
  13. Chao Yu, Guozhen Tan, The Price of Governance: A Middle Ground Solution to Coordination in Organizational Control, IJCAI2019.
  14. Yaodong yang, Jianye Hao and Chao Yu, Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Reinforcement Learning Framework. IJCAI2019
  15. Chao Yu, Xin Wang, Zhanbo Feng: Coordinated Multiagent Reinforcement Learning for Teams of Mobile Sensing Robots. AAMAS 2019: 2297-2299
  16. Chao Yu, Guoqi Ren and Jiming Liu, Deep Inverse Reinforcement Learning for Sepsis Treatment, 2019 IEEE International Conference on Healthcare Informatics, 2019. (EI)
  17. Chao Yu, Yinzhao Dong and Xin Wang, Multiagent Reinforcement Learning on Coordination Graphs, 4th International Workshop on Smart Simulation and Modelling for Complex Systems (SSMCS@IJCAI 2019). (Best Paper Award)
  18. Chao Yu, Dongxu Wang, Jiankang Ren, Hongwei Ge and Liang Sun. Decentralized Multiagent Reinforcement Learning for Efficient Robotic Control by Coordination Graphs. 15th Pacific Rim International Conference on Artificial Intelligence, pp. 191-203, 2018.
  19. Chao Yu, Dongxu Wang, Tianpei Yang, Wenxuan Zhu, Yuchen Li, Hongwei Ge and Jiankang Ren. Adaptively Shaping Reinforcement Learning Agents via Human Reward. 15th Pacific Rim International Conference on Artificial Intelligence, pp. 85-97, 2018. (Best Paper Nomination, 5 out of 441)
  20. Chao Yu, Yatong Chen, Hongtao Lv, Jiankang Ren, Hongwei Ge and Liang Sun. Neural learning for the emergence of social norms in multiagent systems. 2017 IEEE International Conference on Agents (ICA), pp. 40-45, 2017.
  21. Chao Yu, Hongtao Lv, Sandip Sen, Jianye hao, Fenghui Ren and Rui Liu. An Adaptive Learning Framework for Efficient Emergence of Social Norms. 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2016), Singapore. pp. 1307-1308, 2016.
  22. Chao Yu, Hongtao Lv, Sandip Sen, Fenghui Ren and Guozhen Tan. Adaptive Learning for Efficient Emergence of Social Norms in Networked Multiagent Systems. In The Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016): Trends in Artificial Intelligence. LNAI 9810, pp. 805-818, 2016.
  23. Chao Yu, Minjie Zhang, Fenghui Ren and Xudong Luo. Emergence of Social Norms Through Collective Learning in Networked Agent Societies. The Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS2013) , pp.475-482, May 6-10, 2013, Saint Paul, USA.
  24. Chao Yu, Fenghui Ren and Minjie Zhang. An Adaptive Bilateral Negotiation Model Based on Bayesian Learning. The 4th AAMAS International Workshop on Agent-based Complex Automated Negotiations (ACAN@AAMAS2011), The Best Student Paper Award, Taipei, 2011

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