Si-Qi LIU Staff Research Engineer @ DeepMind MPhil/PhD @ UCL, London
Previously CentraleSupélec, University of Oxford.


I'm a Research Engineer at DeepMind where I work on challenging open problems in machine learning and artificial intelligence. My current focus sits at the intersection of Game Theory and Multi-Agent Reinforcement Learning.

I grew up in China, moved to France in 2011 to study and I'm now living and working in London.


NfgTransformer: Equivariant Representation Learning for Normal-form Games

Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp, Nicolas Heess

ICLR 2024 | paper | code

Neural Population Learning beyond Symmetric Zero-sum Games

Siqi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess

AAMAS 2024 | paper

Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers

Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls

NeurIPS 2022 | paper

Developing, evaluating and scaling learning agents in multi-agent environments

Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R McKee, Paul Muller, Julien Pérolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

AI Communications 2022 | paper

From Motor Control to Team Play in Simulated Humanoid Football

Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess

Science Robotics 2022 | paper | blog | demo | code

Simplex NeuPL: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games

Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess

ICML 2022 | paper | spotlight

NeuPL: Neural Population Learning

Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel

ICLR 2022 | paper | demo | poster

Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity

Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hasselt, David Balduzzi

AAMAS 2021 | paper

dm_control: Software and Tasks for Continuous Control

Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, Yuval Tassa

Software Impacts 2020 | paper | code

V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control

H Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin Riedmiller, Matthew M Botvinick

ICLR 2020 | paper

A Generalized Training Approach for Multiagent Learning

Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos

ICLR 2020 | paper

Emergent Coordination through Competition

Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel

ICLR 2019 | paper | website | code

Hierarchical Visuomotor Control of Humanoids

Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Greg Wayne

ICLR 2019 | paper | demo

dm_env: a Python interface for reinforcement learning environments

Alistair Muldal, Yotam Doron, John Aslanides, Tim Harley, Tom Ward, Siqi Liu

github 2019 | code

Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems

Peter Sunehag, Guy Lever, Siqi Liu, Josh Merel, Nicolas Heess, Joel Z Leibo, Edward Hughes, Tom Eccles, Thore Graepel

ALIFE 2019 | paper

The Body is not a Given: Joint Agent Policy Learning and Morphology Evolution

Dylan Banarse, Yoram Bachrach, Siqi Liu, Chrisantha Fernando, Nicolas Heess, Pushmeet Kohli, Guy Lever, Thore Graepel

AAMAS 2019 | paper

Observational Learning by Reinforcement Learning

Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Remi Munos, Olivier Pietquin

AAMAS 2019 | paper

Improved Image Captioning via Policy Gradient Optimization of SPIDER

Siqi Liu, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, Kevin Murphy

ICCV 2017 | paper | oral


Mar 2017 - Present
Staff Research Engineer @ DeepMind
Jul 2016 - Nov 2016
Research Intern @ Google Research
Machine Intelligence (Mountain View)
Jun 2015 - Sep 2015
Software Engineer Intern @ Google
Adwords Optimization (New York)
Feb 2015 - Jun 2015 (part-time)
Consultant Engineer @ Collabora Ltd
LibreOffice for Android
Jun 2014 - Sep 2014
Software Engineer Intern @ Google
Knowledge Graph (Zurich)


2020 - Present
MPhil/PhD in Artificial Intelligence & Neuroscience
2015 - 2016
MSc in Computer Science
2013 - 2016
MEng (Diplôme d'Ingénieur)
2011 - 2013
Génie Informatique