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 | codeNeural Population Learning beyond Symmetric Zero-sum Games
Siqi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess
AAMAS 2024 | paperTurbocharging 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 | paperDeveloping, 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 | paperFrom 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 | codeSimplex NeuPL: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess
ICML 2022 | paper | spotlightNeuPL: Neural Population Learning
Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel
ICLR 2022 | paper | demo | posterPick 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 | paperdm_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 | codeV-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 | paperA 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 | paperEmergent Coordination through Competition
Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel
ICLR 2019 | paper | website | codeHierarchical Visuomotor Control of Humanoids
Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Greg Wayne
ICLR 2019 | paper | demodm_env: a Python interface for reinforcement learning environments
Alistair Muldal, Yotam Doron, John Aslanides, Tim Harley, Tom Ward, Siqi Liu
github 2019 | codeReinforcement 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 | paperThe 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 | paperObservational Learning by Reinforcement Learning
Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Remi Munos, Olivier Pietquin
AAMAS 2019 | paper