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 / Twitter / LinkedIn

News

(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

Publications (representative papers are highlighted)

	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 arXiv
	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 arXiv
	Proximal Learning With Opponent-Learning Awareness Stephen Zhao, Chris Lu, Roger Baker Grosse, Jakob Foerster NeurIPS 2022 arXiv
	Adversarial Cheap Talk Chris Lu, Timon Willi, Alistair Letcher, Jakob Foerster Workshop on Machine Learning for Cybersecurity @ ICML 2022 (Spotlight) Decision Awareness in Reinforcement Learning Workshop @ ICML 2022 Adversarial Machine Learning Frontiers @ ICML 2022 Workshop on Responsible Decision Making in Dynamic Environments @ ICML 2022 Principles of Distribution Shift @ ICML 2022 arXiv
	Discovered Policy Optimisation Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schroeder de Witt, Jakob Foerster Equal Contribution NeurIPS 2022 Decision Awareness in Reinforcement Learning Workshop @ ICML 2022 (Spotlight)* arXiv \| video
	Model-Free Opponent Shaping Chris Lu, Timon Willi, Christian Schroeder de Witt, Jakob Foerster ICML 2022 (Spotlight) ICLR 2022 Workshop on Gamification and Multiagent Solutions (Spotlight) arXiv \| video \| code
	Centralized Model and Exploration Policy for Multi-Agent RL Qizhen Zhang, Chris Lu, Animesh Garg, Jakob Foerster AAMAS 2022 (Oral Presentation) arXiv \| video
	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) webpage \| arXiv \| video \| code

Extended Abstracts

Arbitrary Order Meta-Learning with Simple Population-Based Evolution
Chris Lu, Sebastian Towers, Jakob Foerster
arXiv Preprint

arXiv

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.

Credit for the template to Jon Barron.