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Personal Information

I am Hongyao Tang, a Phd student in Deep Reinforcement Learning (DRL) Lab, Tianjin University advised by Jianye Hao.

My research interest lies in Reinforcement Learning, Representation Learning and Deep Learning. I am curious about the interplay between off-policy RL and representation learning, and how representations in RL can drive new development of DRL.

I write research blogs for my own and TJU DRL Lab's Zhihu channels, and I contribute to Self-supervised RL branch of TJU DRL Lab's github repo.

Recent Publications & Preprints

PS: Authors with equal contribution are marked by *.

ERL-Re^2: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation
Pengyi Li*, Hongyao Tang*, Jianye Hao, Yan Zheng, Xian Fu, Zhaopeng Meng
DRL Workshop, NeurIPS 2022 | [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]

PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang
ICML 2022 | [Paper]

PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
Tong Sang*, Hongyao Tang*, Yi Ma, Jianye Hao, Yan Zheng, Zhaopeng Meng, Boyan Li, Zhen Wang
IJCAI 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]

An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
Tianpei Yang*, Weixun Wang*, Hongyao Tang*, Jianye Hao, Zhaopeng Meng, Hangyu Mao, Dong Li, Wulong Liu, Yingfeng Chen, Yujing Hu, Changjie Fan, Chengwei Zhang
NeurIPS 2021 | [Paper]

Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Hongyao Tang, Zhaopeng Meng, Guangyong Chen, Pengfei Chen, Chen Chen, Yaodong Yang, Luo Zhang, Wulong Liu, Jianye Hao
AAAI 2021 | [Paper]

Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
Haotian Fu, Hongyao Tang, Jianye Hao, Chen Chen, Xidong Feng, Dong Li, Wulong Liu
AAAI 2021 & NeurIPS 2020 DRL Workshop | [Paper]

Addressing Action Oscillations through Learning Policy Inertia
Chen Chen*, Hongyao Tang*, Jianye Hao, Wulong Liu, Zhaopeng Meng
AAAI 2021 | [Paper]

Exploration in Deep Reinforcement Learning: A Comprehensive Survey
Tianpei Yang, Hongyao Tang, Chenjia Bai, Jinyi Liu, Jianye Hao, Zhaopeng Meng, Peng Liu
arXiv preprint (2021) | [Paper]

More Publications

Invited Talks

2022.07 & 2022.06 & 2022.01

Policy-extended Value Function Approximator and Policy Representation in Reinforcement Learning
The 9th China Computer Federation Seminar on Agent and Multi-agent System (2022 Jul)
RL China Community Weakly Seminar (2022 Jun)
The 3rd International Conference on Distributed Artificial Intelligence (2022 Jan)


Towards Understanding The Learning Dynamics of Deep Reinforcement Learning
Huawei Noah’s Ark Lab, Decision-making and Reasoning Group (during internship)


Self-supervised Reinforcement Learning — A Perspective of Representation
2021 TJU RL Summer Seminar


Reward-agnostic Unsupervised State Representation in Deep Reinforcement Learning
Huawei Noah’s Ark Lab, Decision-making and Reasoning Group (during internship)


State Abstraction and State Representation Learning in Reinforcement Learning
Huawei Noah’s Ark Lab, Decision-making and Reasoning Group (during internship)


Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
The 1st International Conference on Distributed Artificial Intelligence (DAI 2019)


Bias and Variance in Deep Reinforcement Learning
2019 TJU RL Summer Seminar

Academic Service

NeurIPS 2022

Top Reviewer

ICLR 2022

Highlighted Reviewer

Education & Work (Internship) Experiences

2020.05 - Present

DRL Researcher (Intern), Noah's Ark Lab, Huawei (advised by Chen Chen and Zhentao Tang)

2019.09 - 2020.04

AI Researcher (Intern), Quantum Lab, Tencent (advised by Guangyong Chen)

2019.09 - Present

Phd Candidate, College of Intelligence and Computing, Tianjin University (advised by Jianye Hao)

2018.07 - 2018.10

DRL Researcher (Intern), Fuxi AI Lab, NetEase (advised by Tangjie Lv)

2017.09 - 2019.07

Master, College of Intelligence and Computing, Tianjin University (advised by Jianye Hao)

2013.09 - 2017.07

Bachelor, School of Software Engineering, Tianjin University

Updated by Hongyao Tang, 2022.