Jianhong Wang
Jianhong Wang is a Senior Research Associate for INFORMED AI Hub, based in the School of Engineering Mathematics and Technology, University of Bristol, working with Prof Jonathan Lawry.
Jianhong received his Ph.D. in Electrical and Electronic Engineering Research from Imperial College London, UK in 2024.
He is also a member of the European Lab for Learning and Intelligent Systems (ELLIS).
Research
Jianhong’s primary research interests revolve around:
- Multi-Agent Reinforcement Learning
- Robust Reinforcement Learning
- Ad Hoc Teamwork
Regarding Multi-Agent Reinforcement Learning and Ad Hoc Teamwork, his emphasis lies in designing algorithms through the lens of Cooperative Game Theory. Integrating fundamental mechanisms from Game Theory into the development of learning algorithms can shed light on interpretability, transparency and reliability of contemporary learning-based Multi-Agent Systems.
Beyond theoretical endeavours, he is passionate about the practical applications of Machine Learning algorithms, particularly Multi-Agent Reinforcement Learning, to Autonomous Systems, for example: Smart Grids, Robotics, Dialogue Systems, etc.
Selected works
- Open Ad Hoc Teamwork with Cooperative Game Theory, accepted at ICML 2024
- SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning, accepted at NeurIPS 2022
- Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks, accepted at NeurIPS 2021
- Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-Oriented Dialogue System, accepted at ICLR 2021
- Shapley Q-value: A Local Reward Approach to Solve Global Reward Games, accepted at AAAI 2020 (Oral)