About Me

Yaodong is a machine learning researcher with almost ten years of experience in both academic research and industrial applications in finance/high-tech companies. Currently, he is an assistant professor at King's College London. His research is about reinforcement learning and multi-agent systems. He has maintained a track record of more than 30 publications at top conferences/journals, along with the best system paper award at CoRL 2020 (first author) and the best blue-sky paper award at AAMAS 2021 (first author). Before KCL, he was a principal research scientist at Huawei UK where he headed the multi-agent system team in London, working on autonomous driving applications. Before Huawei, he was a senior research manager at AIG, working on AI applications in finance. He holds a PhD degree from UCL, an MSc degree from Imperial College London and a Bachelor degree from USTC.  

 

Recent News (2021)

08/2021

Invited talk at RLChina on the tutorial of Multi-Agent Learning.

Slides  Video (in Chinese)

07/2021

Invited talk by 机器之心 on my recent work on how to deal with non-transitivity in two-player zero-sum games.

Slides  Video (in Chinese)

06/2021

We opensource MALib: A bespoke high-performance framework for population-based multi-agent reinforcement learning.

 

Paper  Github

05/2021

Two papers get accepted in ICML 2021.

Modelling Behavioural Diversity for Learning in Open-Ended Games. This paper studies how to measure and promote behavioural diversity in solving games in a mathematically rigorous way. It is awarded a long talk (top 3%) at ICML 2021.

Learning in Nonzero-Sum Stochastic Games with Potentials. This paper studies a generalised class of fully-cooperative games, named stochastic potential games, and propose a MARL solution to find the Nash in such games. 

03/2021

Check out my recent talk on the topic of:

A general framework for solving two-player zero-sum games. 

02/2021

Update: Our paper wins the Best Paper Award at the Blue Sky Idea track!!!

One paper gets accepted in AAMAS 2021.

Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. I express some of my recent thoughts on why behavioural diversity in the policy space is an important factor for MARL techniques to be applied in real-world problems, outside purely video games. 

11/2020

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 :)

10/2020

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

10/2020

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.

09/2020

One paper gets accepted at CIKM 2020 !

Learning to infer user hidden states for online sequential advertising.

08/2020

A lecture was given at RL China Summer School.

Advances of Multi-agent Learning in Gaming AI.

06/2020

A talk was given at ISTBI, Fudan University.

Many-agent Reinforcement Learning.

06/2020

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.

05/2020

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.  

02/2020

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.