Personal profile
Hello there, I am currently an Assistant Professor at School of Intelligence Science and Technology at Nanjing University (Suzhou). Before this, I was a Postdoctoral Researcher in Intelligent Robot Learning Lab at the University of Alberta, advised by Matthew E. Taylor from 2021 to 2024. I received my Ph.D. in 2021 in the College of Intelligence and Computing at Tianjin University, supervised by Jianye Hao. My research interests focus on deep reinforcement learning (DRL) and multiagent systems. I have worked on exploring how to facilitate efficient, scalable RL and multiagent RL through transfer learning, hierarchical RL, and opponent modeling. I am also interested in model-based reinforcement learning, exploration in RL, and human-in-the-loop RL.
I am currently serving as a reviewer for JMLR, TPAMI, TNNLS, TMLR, MACH, JMLC, IEEE TCDS, and IEEE/CAA, and a member of the (senior) program committee (NeurIPS, AAAI, ICLR, IJCAI, ICML, AAMAS, UAI, ICRA, CoRL, CIKM, ECAI, DAI).
I am looking for PhD students/research assistants starting in Fall 2025 or later, interested in (deep) reinforcement learning, multiagent learning, AI agent, or other exciting topics that attract you. If you are interested and have good programming skills and a machine learning background, please email me your CV and transcripts.
Recent News
Year of 2024
🆕[Oct 2024] I will join School of Intelligence Science and Technology at Nanjing University (Suzhou) this winter as an Assistant Professor!
🆕[Aug 2024] I was invited to give a talk at InterPol workshop 2024 at RLC 2024!
🆕[Mar 2024] One paper (FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning) has been accepted by ISEDA 2024!
🆕[Feb 2024] Our paper (LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models) received a Best Paper Runner-Up award from the HCRL@AAAI-24 workshop!
Year of 2023
🆕[Dec 2023] Two papers (PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning, Mastering Robot Control through Point-based Reinforcement Learning with Pre-training) have been accepted by AAMAS 2024!
🆕[Dec 2023] Two papers (A Transfer Approach Using Graph Neural Networks in Deep Reinforcement Learning, PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning) have been accepted by AAAI 2024! One paper (LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models) has been accepted by AAAI 2024 Workshop on Human-Centric Representation Learning!
🆕[Nov 2023] Our paper (Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities) has been accepted by Journal of Artificial Intelligence Research (JAIR)!
🆕[Oct 2023] Two papers (PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning, Work-in-Progress: Using Symbolic Planning with Deep RL) have been accepted by GenPlan Workshop NeurIPS 2023! One paper (Reinforcement Learning for FPGA Placement) has been accepted by MlSys Workshop NeurIPS 2023!
🆕[Oct 2023] Our paper (ASN: Action Semantics Network for Multiagent Reinforcement Learning) has been accepted by Autonomous Agents and Multi-Agent Systems (JAAMAS)!
🆕[July-Sep 2023] I was invited as a PC of AAAI 2024, ICRA 2024, ICLR 2024, and AAMAS 2024!
🆕[May 2023] I was invited as an area chair of (MRS 2023)!
🆕[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 IEEE Transactions on Neural Networks and Learning Systems (TNNLS)! 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!
Year of 2022
🆕 [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! One paper (Efficient Deep Reinforcement Learning via Policy-extended Successor Feature Approximator) got accepted at DAI 2022!
🆕 [Sep 2022] Our paper (GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis) got accepted at NeurIPS 2022!
🆕 [May 2022] Our paper (Cross-domain Adaptive Transfer Reinforcement Learning Based on State-Action Correspondence) got accepted at UAI 2022! One paper (PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration) got accepted at ICML 2022!
Publications
A Transfer Approach Using Graph Neural Networks in Deep Reinforcement Learning. Tianpei Yang et al. AAAI. 2024.
ASN: Action Semantics Network for Multiagent Reinforcement Learning. Tianpei Yang et al. JAAMAS. 2024. url
Exploration in Deep Reinforcement Learning: From Single-Agent to Multi-Agent Domain. Jianye Hao, 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.
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
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