Chris Lu
I am a second-year DPhil student at the University of Oxford, where I am
advised by Professor Jakob
Foerster at FLAIR. My work focuses on applying
evolution-inspired techniques to meta-learning and multi-agent
reinforcement learning. In the summer of 2022 I interned at DeepMind as a research scientist.
Previously, I worked as a researcher at Covariant.ai.
Email  / 
Google Scholar
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Twitter  / 
LinkedIn
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News
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(04/2023) I released a blog post on PureJaxRL, a JAX-based RL library that
massively speeds up RL training!
- (03/2023) I gave talks on evolutionary meta-learning at the UMD
Multi-Agent Reinforcement Learning Reading Group, the AI4ABM Reading Group, and the Learning in Foundation
Environments (LIFE) Reading Group!
- (09/2022) We received a GoodAI
grant, which I co-wrote on behalf of the lab
- (07/2022) I gave in-person talks on three of our papers at the main conference and several
workshops at ICML 2022 in Baltimore!
[Model-Free Opponent Shaping, Discovered Policy Optimisation, and Adversarial Cheap Talk]
- (06/2022) I started an internship at DeepMind as a
Research Scientist on the Discovery Team!
- (05/2022) We received an Oracle for Research Grant, which I co-wrote on behalf of the lab
- (10/2021) I started my DPhil at Oxford with FLAIR
Blog Posts
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Publications (representative papers are highlighted)
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Adversarial Cheap Talk
Chris Lu, Timon Willi, Alistair Letcher, Jakob Foerster
ICML 2023
Also at the Workshop on Machine Learning for Cybersecurity @ ICML 2022 (Spotlight)
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Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization
Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dallibard,
Sebastian Flennerhag
GECCO 2023
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Arbitrary Order Meta-Learning with Simple Population-Based Evolution
Chris Lu, Sebastian Towers, Jakob Foerster
ALIFE 2023 (Oral)
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Discovering Evolution Strategies via Meta-Black-Box Optimization
Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dallibard, Chris Lu,
Satinder Singh, Sebastian Flennerhag
ICLR 2023
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Proximal Learning With Opponent-Learning Awareness
Stephen Zhao, Chris Lu, Roger Baker Grosse, Jakob Foerster
NeurIPS 2022
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Discovered Policy Optimisation
Chris Lu*, Jakub Grudzien Kuba*, Alistair Letcher, Luke Metz, Christian Schroeder de Witt,
Jakob Foerster
*Equal Contribution
NeurIPS 2022
Also at the Decision Awareness in Reinforcement Learning Workshop @ ICML 2022
(Spotlight)
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Model-Free Opponent Shaping
Chris Lu, Timon Willi, Christian Schroeder de Witt, Jakob Foerster
ICML 2022 (Spotlight)
Also at the ICLR 2022 Workshop on Gamification and Multiagent Solutions (Spotlight)
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Centralized Model and Exploration Policy for Multi-Agent RL
Qizhen Zhang, Chris Lu, Animesh Garg, Jakob Foerster
AAMAS 2022 (Oral Presentation)
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Learning to Control Self-Assembling Morphologies
Deepak Pathak*, Chris Lu*, Trevor Darrell, Phillip Isola, Alexei A. Efros
*Equal Contribution
NeurIPS 2019 (Spotlight)
Winner of Virtual Creatures Competition (link)
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Preprints and Workshop Papers
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ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
Andrew Jesson, Chris Lu, Gunshi Gupta, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal
arXiv Preprint
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Structured State Space Models for In-Context Reinforcement Learning
Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob Foerster, Satinder Singh,
Feryal Behbahani
arXiv Preprint
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Scaling Opponent Shaping to High Dimensional Games
Akbir Khan*, Timon Willi*, Newton Kwan*, Andrea Tachetti, Chris Lu, Edward Grefenstette, Tim
Rocktäschel, Jakob Foerster
*Equal Contribution
Games, Agents, and Incentives Workshop at AAMAS 2023
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Misc
- Reviewer for: NeurIPS 2021, ICLR 2022, IROS 2022, NeurIPS 2022, ALOE@ICLR2022,
DARL@ICML2022,
AI4ABM@ICML2022
- In my free time I like to work on side projects. I used to sell kalimbas! I
also solo-developed and sold a video game!
- If you want to see some of my older works and projects, my old website is here.
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