Machine Learning Researcher
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About Me

Yaodong is a principal research scientist at Huawei UK where he heads the multi-agent learning research team. He has published more than 30 machine learning/AI research papers and patents including NIPS/ICML/ICLR/AAAI/IJCAI/CoRL (best paper award). Before joining Huawei UK, he was a senior science manager at AIG, working on machine learning applications in finance. He holds a PhD degree from UCL and an MSc degree from Imperial College London and a B.Eng. degree from USTC. In 2018, he was awarded UK Exceptional Talent in AI by the Home Office. 

 

Recent News (2021)

03/2021

Check out my recent talk on the topic of:

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

02/2021

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.