I am a machine learning researcher focusing on reinforcement learning, multi-agent learning, and Bayesian inference. Currently, I am the techlead of multi-agent learning at Huawei Noah's Ark (AI Lab), responsible for delivering research work on multi-agent system and its application for autonomous driving. Before joining Huawei, I was a senior manager at the Science department of American International Group, where I led a machine learning research team to develop AI-powered methodology innovations for insurance problems. In 2018, I was awarded UK Exceptional Talent in Machine Learning/AI by the Home Office.
Recent News (2020)
Check out my latest work on:
An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective. I hope this work could offer a nice summary of game theory basics for MARL researches in addition to the deep RL hype :)
Update: SMARTS won the BEST paper award in CoRL 2020!
We release SMARTS: a multi-agent reinforcement learning enabled autonomous driving platform.
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Today we are excited to introduce a dedicated platform: SMARTS, that supports Scalable Multi-Agent Reinforcement Learning Training for autonomous driving. With SMARTS, ML researchers can now evaluate their new algorithms in the self-driving scenarios, in addition to traditional video games. In turn, SMARTS can enrich the social vehicle behaviours and create increasingly more realistic and diverse interactions, powered by RL techniques, for autonomous driving researchers. Check our code on Github, and our paper at Conference on Robotic Learning 2020.
One paper gets accepted at NIPS 2020 !
Replica-exchange Nos\'e-Hoover dynamics for Bayesian learning on large datasets. We introduce a new HMC sampler for large-scale Bayesian deep learning that suits multi-mode sampling and the noises from mini-batches can be absorbed by a special design of Nose-Hoover dynamics.
One paper gets accepted at CIKM 2020 !
A lecture was given at RL China Summer School.
A talk was given at ISTBI, Fudan University.
Many-agent Reinforcement Learning.
One paper gets accepted at ICML 2020
Multi-agent Determinantal Q-learning. We introduce a new function approximator called Q-determinant point process for multi-agent reinforcement learning problems. It can help learn the Q-function factorisation with no needs for a priori structural constraints such as QMIX, VDN, etc.
One paper gets accepted at IJCAI 2020
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning. We use probabilisitic graphical model to describe the recursive reasoning process of "I believe you believe I believe..." in the multi-agent system.
One paper gets accepted at AAMAS 2020
Alpha^Alpla-Rank: Practically Scaling Alpha-Rank through Stochastic Optimisation. Alpha-Rank is a replacement for Nash equilibrium for general-sum N-player game, importantly, its solution is P-complete. In this paper, we further enhance its tractability by several orders of magnitude by stochastic optimisation formulation.