Personal profile
Hello there, I am currently a Postdoctoral Researcher in Intelligent Robot Learning Lab at the University of Alberta, advised by Matthew E. Taylor. I received my Ph.D. in the College of Intelligence and Computing from Tianjin University, supervised by Jianye Hao. I received my M.S. degree at Tianjin University in 2017 and B.E. degree at Tianjin University in 2014. My research interests focus on deep reinforcement learning (DRL) and multiagent systems. I have done some work on exploring how to facilitate efficient, scalable RL and multiagent RL through transfer learning, hierarchical RL, and opponent modeling. I am also involved with several projects including model-based reinforcement learning, exploration in RL and human-in-the-loop RL.
I am currently serving as a reviewer for JMLR, TPAMI, TMLR, IEEE TCDS and IEEE/CAA, a member of program committee (NeurIPS’2020,2021,2022,2023, AAAI’2021,2022,2023, ICLR’2021,2022,2023, IJCAI’2021,2022,2023, ICML’2021,2022,2023, UAI’2023, CoRL’2021,2022,2023, CIKM’2022, ECAI’2020, DAI’2019).
News
🆕[Apr 2023] Our paper (T3S: Improving Multi-Task Reinforcement Learning with Task-Specific Feature Selector and Scheduler) got accepted at IJCNN 2023!
🆕[Mar 2023] I was invited as a PC of NeurIPS 2023 and CoRL 2023!
🆕[Jan 2023] I was invited as a PC of UAI 2023!
🆕[Jan 2023] Our paper (Exploration in Deep Reinforcement Learning: From Single-Agent to Multi-Agent Domain) got accepted at TNNLS!
🆕 [Jan 2023] Two papers (PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning, Transfer Learning based Agent for Automated Negotiation) got accepted at AAMAS 2023 as Extended Abstract!
🆕 [Dec 2022] I was invited as a PC of IJCAI 2023 and ICML 2023!
🆕 [Nov 2022] Our paper (Learning to Shape Rewards using a Game of Two Partners) got accepted at AAAI 2023!
🆕 [Sep 2022] Our paper (GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis) got accepted at NeurIPS 2022!
Publicatoins
Exploration in Deep Reinforcement Learning: From Single-Agent to Multi-Agent Domain. Jianye Hao (Phd Advisor), Tianpei Yang et al. TNNLS. 2023. url
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis. Yushi Cao, Zhiming Li, Tianpei Yang* (Corresponding author) et al. NeurIPS. 2022. url
Cross-domain Adaptive Transfer Reinforcement Learning Based on State-Action Correspondence. Heng You, Tianpei Yang* (Corresponding author) et al. UAI. 2022.
PMIC: Improving Multi-Agent Reinforcement Learning with ProgressiveMutual Information Collaboration. Pengyi Li, Hongyao Tang, Tianpei Yang et al. ICML. 2022. url
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning. Tianpei Yang et al. NeurIPS. 2021. url
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer. Tianpei Yang et al. IJCAI. 2020. url
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems. Weixun Wang (Equal contribution), Tianpei Yang (Equal contribution) et al. ICLR. 2020. url
From Few to More: Large-scale Dynamic Multiagent Curriculum Learning. Weixun Wang (Equal contribution), Tianpei Yang (Equal contribution) et al. AAAI. 2020. url
Towards Efficient Detection and Optimal Response against Sophisticated Opponents. Tianpei Yang et al. IJCAI. 2019. url