Personal Information
Hello, I am Hongyao Tang. I am a postdoctoral researcher with Professor Glen Berseth, in Robotics and Embodied AI Lab (REAL) at the Université de Montréal and Mila. Prior to this, I obtained my Ph.D. (Master and Bachelor Degree as well) in Deep Reinforcement Learning (DRL) Lab, Tianjin University advised by Professor Jianye Hao, Zhaopeng Meng, and Li Wang.
My research interests lie in unveiling the learning dynamics of Deep Reinforcement Learning (DRL) and realizing new approaches/paradigms for efficient, performant and generalizable agents. Currently, my research focus is Learning under Nonstationarity and Self-supervised Representation Learning in DRL. I am also interested in Meta RL, MARL, Offline RL and Foundation Models for Decision Making.
I have experiences in applying DRL in practical problems like Electronic Design Automation (EDA), Drug Discovery, Online Games and etc. I am very willing to contribute to addressing real-world problems.
Recent Publications & Preprints
PS: Authors with equal contribution are marked by *.
Efficient Morphology-Aware Policy Transfer to New Embodiments
Michael Przystupa, Hongyao Tang, Glen Berseth, Mariano Phielipp, Santiago Miret, Martin Jägersand, Matthew E. Taylor
RLC 2025
| [Paper]
Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
Hongyao Tang, Johan Obando-Ceron, Pablo Samuel Castro, Aaron Courville, Glen Berseth
ICML 2025
| [Paper]
R*: Efficient Reward Design via Reward Structure Evolution and Parameter Alignment Optimization with Large Language Models
Pengyi Li, Jianye Hao, Hongyao Tang, Yifu Yuan, Jinbin Qiao, Zibin Dong, Yan Zheng
ICML 2025
| [Paper]
Can We Optimize Deep RL Policy Weights as Trajectory Modeling?
Hongyao Tang
ICLR 2025 Workshop on Weight Space Learning
| [Paper]
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
Hongyao Tang, Glen Berseth
NeurIPS 2024
| [Paper]
[Code]
The Ladder in Chaos: Improving Policy Learning by Harnessing the Parameter Evolving Path in A Low-dimensional Space
Hongyao Tang, Min Zhang, Chen Chen, Jianye Hao
NeurIPS 2024
| [Paper]
EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search
Pengyi Li, Jianye Hao, Hongyao Tang, Xian Fu, Yan Zheng
ICML 2024
| [Paper]
[Code]
Reining Generalization in Offline Reinforcement Learning via Representation Distinction
Yi Ma, Hongyao Tang (Corresponding Author), Dong Li, Zhaopeng Meng
NeurIPS 2023
| [Paper]
Exploration in Deep Reinforcement Learning: From Single-Agent to Multi-Agent Domain
Jianye Hao, Tianpei Yang, Hongyao Tang, Chenjia Bai, Jinyi Liu, Zhaopeng Meng, Peng Liu
IEEE Transactions on Neural Networks and Learning Systems (Accepted in 2023 Jan)
| [Paper]
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes
Min Zhang*, Hongyao Tang*, Jianye Hao, Yan Zheng
DRL Workshop, NeurIPS 2022
| [Paper]
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Boyan Li*, Hongyao Tang*, Yan Zheng, Jianye Hao, Pengyi Li, Zhen Wang, Zhaopeng Meng, Li Wang
ICLR 2022 & NeurIPS 2021 DRL Workshop Contributed Talk
| [Paper]
What about Inputting Policy in Value Function: Policy Representation and Policy-Extended Value Function Approximator
Hongyao Tang, Zhaopeng Meng, Jianye Hao, Chen Chen, Daniel Graves, Dong Li, Changmin Yu, Hangyu Mao, Wulong Liu, Yaodong Yang, Wenyuan Tao, Li Wang
AAAI 2022 Oral Presentation (< 5%) & NeurIPS 2020 DRL Workshop
| [Paper]
More Publications
Education & Work (Internship) Experiences
2023.11 - Present
Postdoctoral Researcher, Robotics and Embodied AI Lab (REAL), UdeM/Mila (work with Glen Berseth)
2019.09 - 2023.06
Phd, College of Intelligence and Computing, Tianjin University (advised by Jianye Hao and Zhaopeng Meng)
2020.05 - 2023.04
DRL Researcher (Intern), Noah's Ark Lab, Huawei (work with Chen Chen and Zhentao Tang)
2019.09 - 2020.04
AI Researcher (Intern), Quantum Lab, Tencent (work with Guangyong Chen)
2018.07 - 2018.10
DRL Researcher (Intern), Fuxi AI Lab, NetEase (work with Tangjie Lv)
2017.09 - 2019.07
Master, College of Intelligence and Computing, Tianjin University (advised by Jianye Hao and Li Wang)
2013.09 - 2017.07
Bachelor, School of Software Engineering, Tianjin University
Academic Service
RLC
2024, 2025
NeurIPS
2021 - 2025 (Top Reviewer Award at NeurIPS 2022)
ICLR
2022 - 2025 (Highlighted Reviewer Award at ICLR 2022)
ICML
2021 - 2025
AAAI
2021 - 2025
IJCAI
2021 - 2024
AAMAS
2021 - 2024
Transactions on Machine Learning Research (TMLR)
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
Invited Talks
2025.03
RL in the Era of Large Models
2025 IEEE International Conference on Industrial Technology
2024.10
Where is the Road to Flawless RL? — Unsolved Problems and New Approaches
Google DeepMind, Discovery Team London
2023.05
A Tale of Representations in Deep Reinforcement Learning
Robotics and Embodied AI Lab (REAL), the Université de Montréal
2022.07
Policy-extended Value Function Approximator and Policy Representation in Reinforcement Learning
The 9th China Computer Federation Seminar on Agent and Multi-agent System
2022.07
Towards Understanding The Learning Dynamics of Deep Reinforcement Learning
Huawei Noah’s Ark Lab, Decision-making and Reasoning Group (during internship)
2021.10
Self-supervised Reinforcement Learning — A Perspective of Representation
2021 TJU RL Summer Seminar
2021.06
Reward-agnostic Unsupervised State Representation in Deep Reinforcement Learning
Huawei Noah’s Ark Lab, Decision-making and Reasoning Group (during internship)
2020.11
State Abstraction and State Representation Learning in Reinforcement Learning
Huawei Noah’s Ark Lab, Decision-making and Reasoning Group (during internship)
2019.10
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
The 1st International Conference on Distributed Artificial Intelligence (DAI 2019)
2019.08
Bias and Variance in Deep Reinforcement Learning
2019 TJU RL Summer Seminar