Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity. - Deepak Pathak; Christopher Lu 001; Trevor Darrell; Phillip Isola; Alexei A. Efros (NeurIPS, 2019)

Centralized Model and Exploration Policy for Multi-Agent RL. - Qizhen Zhang 002; Christopher Lu 001; Animesh Garg; Jakob N. Foerster (AAMAS, 2022)

Model-Free Opponent Shaping. - Christopher Lu 001; Timon Willi; Christian A. Schröder de Witt; Jakob N. Foerster (ICML, 2022)

Discovered Policy Optimisation. - Chris Lu 001; Jakub Grudzien Kuba; Alistair Letcher; Luke Metz; Christian Schröder de Witt; Jakob N. Foerster (NeurIPS, 2022)

Proximal Learning With Opponent-Learning Awareness. - Stephen Zhao; Chris Lu 001; Roger B. Grosse; Jakob N. Foerster (NeurIPS, 2022)

Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization. - Robert Tjarko Lange; Tom Schaul; Yutian Chen 001; Chris Lu 001; Tom Zahavy; Valentin Dalibard; Sebastian Flennerhag (GECCO, 2023)

Discovering Evolution Strategies via Meta-Black-Box Optimization. - Robert Tjarko Lange; Tom Schaul; Yutian Chen 001; Tom Zahavy; Valentin Dalibard; Chris Lu 001; Satinder Singh 001; Sebastian Flennerhag (GECCO Companion, 2023)

JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading. - Sascha Yves Frey; Kang Li; Peer Nagy; Silvia Sapora; Christopher Lu 001; Stefan Zohren; Jakob N. Foerster; Anisoara Calinescu (ICAIF, 2023)

Adversarial Cheap Talk. - Chris Lu 001; Timon Willi; Alistair Letcher; Jakob Nicolaus Foerster (ICML, 2023)

Structured State Space Models for In-Context Reinforcement Learning. - Chris Lu 001; Yannick Schroecker; Albert Gu; Emilio Parisotto; Jakob N. Foerster; Satinder Singh 001; Feryal M. P. Behbahani (NeurIPS, 2023)

Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design. - Matthew Thomas Jackson; Minqi Jiang; Jack Parker-Holder; Risto Vuorio; Chris Lu 001; Gregory Farquhar; Shimon Whiteson; Jakob N. Foerster (NeurIPS, 2023)

Arbitrary Order Meta-Learning with Simple Population-Based Evolution. - Chris Lu 001; Sebastian Towers; Jakob N. Foerster (CoRR, 2023)

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. - Andrew Jesson; Chris Lu 001; Gunshi Gupta; Angelos Filos; Jakob Nicolaus Foerster; Yarin Gal (CoRR, 2023)

JaxMARL: Multi-Agent RL Environments in JAX. - Alexander Rutherford; Benjamin Ellis; Matteo Gallici; Jonathan Cook 004; Andrei Lupu; Garðar Ingvarsson; Timon Willi; Akbir Khan; Christian Schröder de Witt; Alexandra Souly; Saptarashmi Bandyopadhyay; Mikayel Samvelyan; et al. (CoRR, 2023)

Leading the Pack: N-player Opponent Shaping. - Alexandra Souly; Timon Willi; Akbir Khan; Robert Kirk; Chris Lu 001; Edward Grefenstette; Tim Rocktäschel (CoRR, 2023)

Analysing the Sample Complexity of Opponent Shaping. - Kitty Fung; Qizhen Zhang 002; Chris Lu 001; Jia Wan; Timon Willi; Jakob N. Foerster (AAMAS, 2024)

Scaling Opponent Shaping to High Dimensional Games. - Akbir Khan; Timon Willi; Newton Kwan; Andrea Tacchetti; Chris Lu 001; Edward Grefenstette; Tim Rocktäschel; Jakob N. Foerster (AAMAS, 2024)

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX. - Alexander Rutherford; Benjamin Ellis; Matteo Gallici; Jonathan Cook 004; Andrei Lupu; Garðar Ingvarsson; Timon Willi; Akbir Khan; Christian Schröder de Witt; Alexandra Souly; Saptarashmi Bandyopadhyay; Mikayel Samvelyan; et al. (AAMAS, 2024)

Discovering Temporally-Aware Reinforcement Learning Algorithms. - Matthew Thomas Jackson; Chris Lu 001; Louis Kirsch; Robert Tjarko Lange; Shimon Whiteson; Jakob Nicolaus Foerster (ICLR, 2024)

Behaviour Distillation. - Andrei Lupu; Chris Lu 001; Jarek Liesen; Robert Tjarko Lange; Jakob Nicolaus Foerster (ICLR, 2024)

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. - Andrew Jesson; Chris Lu 001; Gunshi Gupta; Nicolas Beltran-Velez; Angelos Filos; Jakob Nicolaus Foerster; Yarin Gal (ICML, 2024)

EvIL: Evolution Strategies for Generalisable Imitation Learning. - Silvia Sapora; Gokul Swamy; Chris Lu 001; Yee Whye Teh; Jakob Nicolaus Foerster (ICML, 2024)

Discovering Preference Optimization Algorithms with and for Large Language Models. - Chris Lu 001; Samuel Holt; Claudio Fanconi; Alex J. Chan; Jakob N. Foerster; Mihaela van der Schaar; Robert T. Lange (NeurIPS, 2024)

Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning. - Jonathan Cook 004; Chris Lu 001; Edward Hughes 001; Joel Z. Leibo; Jakob N. Foerster (NeurIPS, 2024)

Can Learned Optimization Make Reinforcement Learning Less Difficult? - Alexander David Goldie; Chris Lu 001; Matthew Thomas Jackson; Shimon Whiteson; Jakob N. Foerster (NeurIPS, 2024)

Recurrent Reinforcement Learning with Memoroids. - Steven D. Morad; Chris Lu 001; Ryan Kortvelesy; Stephan Liwicki; Jakob N. Foerster; Amanda Prorok (NeurIPS, 2024)

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX. - Alexander Rutherford; Benjamin Ellis; Matteo Gallici; Jonathan Cook 004; Andrei Lupu; Garðar Ingvarsson; Timon Willi; Ravi Hammond; Akbir Khan; Christian Schröder de Witt; Alexandra Souly; Saptarashmi Bandyopadhyay; et al. (NeurIPS, 2024)

The Danger Of Arrogance: Welfare Equilibra As A Solution To Stackelberg Self-Play In Non-Coincidental Games. - Jake Levi; Chris Lu 001; Timon Willi; Christian Schröder de Witt; Jakob N. Foerster (CoRR, 2024)

Meta-learning the mirror map in policy mirror descent. - Carlo Alfano; Sebastian Towers; Silvia Sapora; Chris Lu 001; Patrick Rebeschini (CoRR, 2024)

Revisiting Recurrent Reinforcement Learning with Memory Monoids. - Steven D. Morad; Chris Lu 001; Ryan Kortvelesy; Stephan Liwicki; Jakob N. Foerster; Amanda Prorok (CoRR, 2024)

Discovering Minimal Reinforcement Learning Environments. - Jarek Liesen; Chris Lu 001; Andrei Lupu; Jakob N. Foerster; Henning Sprekeler; Robert T. Lange (CoRR, 2024)

NAVIX: Scaling MiniGrid Environments with JAX. - Eduardo Pignatelli; Jarek Liesen; Robert Tjarko Lange; Chris Lu 001; Pablo Samuel Castro; Laura Toni (CoRR, 2024)

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery. - Chris Lu 001; Cong Lu; Robert Tjarko Lange; Jakob N. Foerster; Jeff Clune; David Ha (CoRR, 2024)

JaxLife: An Open-Ended Agentic Simulator. - Chris Lu 001; Michael Beukman; Michael T. Matthews; Jakob N. Foerster (CoRR, 2024)

Automating the Search for Artificial Life with Foundation Models. - Akarsh Kumar; Chris Lu 001; Louis Kirsch; Yujin Tang; Kenneth O. Stanley; Phillip Isola; David Ha (CoRR, 2024)

Learning mirror maps in policy mirror descent. - Carlo Alfano; Sebastian Rene Towers; Silvia Sapora; Chris Lu 001; Patrick Rebeschini (ICLR, 2025)

Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks. - Michael T. Matthews; Michael Beukman; Chris Lu 001; Jakob Nicolaus Foerster (ICLR, 2025)

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search. - Yutaro Yamada; Robert Tjarko Lange; Cong Lu; Shengran Hu; Chris Lu 001; Jakob N. Foerster; Jeff Clune; David Ha (CoRR, 2025)