Personal Information
Hello, I am Hongyao Tang, 汤宏垚. I am an Associate Research Fellow at TJU RL Lab, College of Intelligence and Computing, Tianjin University.
Prior to this, I was a postdoctoral researcher with Professor Glen Berseth, in Robotics and Embodied AI Lab (REAL) at the Mila and Université de Montréal. I obtained my Ph.D. (Master's and Bachelor's 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. My current research focus is on Learning under Nonstationarity (i.e., one intriguing nature of RL), and concretely, I study Continual RL and RL problems in LLMs and embodied intelligence. I am also interested in Meta RL, MARL, Offline RL, Machine Unlearning, etc.
I have experience 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.
⚡️ Highlight
For prospective students (master, ph.d., RA, etc.) and collaboration partners, please contact me via my email (above) or contact via the homepage of TJU RL Lab.
🌟 News
2025.09
We released Embodied Arena 🤖, a comprehensive, unified, and evolving evaluation platform for Embodied AI. Feel free to check the platform and our tech report out!
2025.09
Four papers accepted to NeurIPS 2025 on LLM-driven reward function design, multi-objective RL, offline RL, and EDA floorplanning!
2025.05
One paper accepted to RLC 2025 on efficient cross-morphology adaptation!
2025.05
Two papers accepted to ICML 2025 on continual RL and LLM-driven reward design!
2023.11
Excited to join TJU RL Lab as an Associate Research Fellow! Excited to work with Prof. Glen Berseth.
2024.09
Two papers accepted to NeurIPS 2024 on learning dynamics of DRL!
2023.11
Excited to join Mila/UdeM as a Postdoctoral Research Fellow! It is great a honor to work with Prof. Glen Berseth.
2023.06
Got my Ph.D. at Tianjin University! Grateful to Prof. Jianye Hao and Prof. Zhaopeng Meng.
🛣 Education & Work (Internship) Experiences
2025.01 - Present
Associate Research Fellow, TJU RL Lab, College of Intelligence and Computing, Tianjin University
2023.11 - 2024.12
Postdoctoral Research Fellow, Robotics and Embodied AI Lab (REAL), Mila/UdeM(work with Glen Berseth, and co-author with Johan Obando-Ceron, Pablo Samuel Castro, and Aaron Courville)
2019.09 - 2023.06
Ph.D., 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
🧠 Selected Publications & Preprints
PS: Authors with equal contribution are marked by *. Corresponding authors are marked by 📮.
Embodied Arena: A Comprehensive, Unified, and Evolving Evaluation Platform for Embodied AI
Fei Ni, Min Zhang, Pengyi Li, Yifu Yuan, Lingfeng Zhang, Yuecheng Liu, Peilong Han, Longxin Kou, Shaojin Ma, Jinbin Qiao, David Gamaliel Arcos Bravo, Yuening Wang, Xiao Hu, Zhanguang Zhang, Xianze Yao, Yutong Li, Zhao Zhang, Ying Wen, Ying-Cong Chen, Xiaodan Liang, Liang Lin, Bin He, Haitham Bou-Ammar, He Wang, Huazhe Xu, Jiankang Deng, Shan Luo, Shuqiang Jiang, Wei Pan, Yang Gao, Stefanos Zafeiriou, Jan Peters, Yuzheng Zhuang, Yingxue Zhang, Yan Zheng, Hongyao Tang, Jianye Hao
arXiv 2025
| [Paper]
LaRes: Evolutionary Reinforcement Learning with LLM-based Adaptive Reward Search
Pengyi Li, Jianye Hao, Hongyao Tang, Jinbin Qiao, Yan Zheng
NeurIPS 2025
| [Paper]
COLA: Towards Efficient Multi-Objective Reinforcement Learning with Conflict Objective Regularization in Latent Space
Pengyi Li, Jianye Hao, Yifu Yuan, Hongyao Tang📮, Zibin Dong, Yan Zheng
NeurIPS 2025
| [Paper]
Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies
Yi Ma*, Hongyao Tang*, Chenjun Xiao, Yaodong Yang, Wei Wei, Jianye Hao, Jiye Liang
arXiv preprint 2025
| [Paper]
Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model
Jing Liang*, Hongyao Tang*, Yi Ma, Jinyi Liu, Yan Zheng, Shuyue Hu, Lei Bai, Jianye Hao
arXiv preprint 2025
| [Paper]
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]
[Code]
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📮, Dong Li, Zhaopeng Meng
NeurIPS 2023
| [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]
[Code]
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]
[Code]
More Publications
Academic Service
RLC
2024, 2025
NeurIPS
2021 - 2025 (Top Reviewer Award at NeurIPS 2022)
ICLR
2022 - 2026 (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 (Remote)
2023.05
A Tale of Representations in Deep Reinforcement Learning
Robotics and Embodied AI Lab (REAL), the Université de Montréal
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
2020.11
State Abstraction and State Representation Learning in Reinforcement Learning
Huawei Noah’s Ark Lab, Decision-making and Reasoning Group (during internship)
2019.08
Bias and Variance in Deep Reinforcement Learning
2019 TJU RL Summer Seminar