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.

Google Scholar / Twitter / Github / LinkedIn

News

(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

Achieving 4000x Speedups and Meta-Evolving Discoveries with PureJaxRL (Tweet)

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 NeurIPS 2023 Also at the Workshop on New Frontiers in Learning, Control, and Dynamical Systems @ ICML 2023 arXiv \| code \| tweet
	Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Gregory Farquhar, Shimon Whiteson, Jakob Nicolaus Foerster NeurIPS 2023 arXiv (TBA) \| code (TBA)
	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) webpage \| arXiv \| code \| tweet
	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 arXiv
	Arbitrary Order Meta-Learning with Simple Population-Based Evolution Chris Lu, Sebastian Towers, Jakob Foerster ALIFE 2023 (Oral) 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 \| video \| code \| tweet
	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) arXiv \| video \| code \| tweet
	Proximal Learning With Opponent-Learning Awareness Stephen Zhao, Chris Lu, Roger Baker Grosse, Jakob Foerster NeurIPS 2022 arXiv \| code \| tweet
	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) arXiv \| video \| code \| tweet
	Centralized Model and Exploration Policy for Multi-Agent RL Qizhen Zhang, Chris Lu, Animesh Garg, Jakob Foerster AAMAS 2022 (Oral Presentation) arXiv \| video \| tweet
	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 \| tweet

Preprints and Workshop Papers

	Analyzing the Sample Complexity of Model-Free Opponent Shaping Kitty Fung, Qizhen Zhang, Chris Lu, Timon Willi, Jakob Foerster ICML 2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems paper \| code
	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 arXiv \| code \| tweet
	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 webpage \| paper
	JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading Sascha Frey, Kang Li, Peer Nagy, Silvia Sapora, Chris Lu, Stefan Zohren, Jakob Foerster, Anisoara Calinescu Equal Contribution arXiv Preprint arXiv

Misc

Reviewer for: NeurIPS 2021, ICLR 2022, IROS 2022, NeurIPS 2022, NeurIPS 2023, ALOE@ICLR2022, DARL@ICML2022, AI4ABM@ICML2022, F4LCD@ICML2023
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.
I also created the Noisy TV environment that appears in a few papers on curiosity-driven learning. The code is here.
If you want to see some of my older works and projects, my old website is here.

Credit for the template to Jon Barron.