Research

Can Learned Optimization Make RL Less Difficult?

Alex Goldie, Chris Lu, Matthew Jackson, Shimon Whiteson, Jakob Foerster

NeurIPS 2024 (Spotlight)

No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery

Alexander Rutherford*, Michael Beukman*, Timon Willi, Bruno Lacerda, Nick Hawes, Jakob Foerster

NeurIPS 2024

Artificial Generational Intelligence: Cultural Accumulation in RL

Jonathan Cook*, Chris Lu*, Edward Hughes, Joel Leibo, Jakob Foerster

NeurIPS 2024

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Alexander Rutherford*†, Benjamin Ellis*†, Matteo Gallici*†, Jonathan Cook†, Andrei Lupu†, Gardar Ingvarsson†, Timon Willi†, Ravi Hammond†, Akbir Khan, Christian Schroeder de Witt, Alexandra Souly, Saptarashmi Bandyopadhyay, Mikayel Samvelyan, Minqi Jiang, Robert Tjarko Lange, Shimon Whiteson, Bruno Lacerda, Nick Hawes, Tim Rocktaschel, Chris Lu*†, Jakob Foerster

NeurIPS Datasets and Benchmarks 2024

Mixtures of Experts for Scaling up Neural Networks in Order Execution

Kang Li, Mihai Cucuringu, Leandro Sánchez-Betancourt, Timon Willi

ICAIF 2024

Policy-Guided Diffusion

Matthew Jackson, Michael Matthews, Cong Lu, Ben Ellis, Shimon Whiteson, Jakob Foerster

RLC 2024

Mixture of Experts in a Mixture of RL Settings

Timon Willi*, Johan Obando-Ceron*, Jakob Foerster, Karolina Dziugaite, Pablo Samuel Castro

RLC 2024

Refining Minimax Regret for Unsupervised Environment Design

Michael Beukman, Samuel Coward, Michael Matthews, Mattie Fellows, Minqi Jiang, Michael Dennis, Jakob Foerster

ICML 2024

Craftax: A Lightning-Fast Benchmark for Open-Ended RL

Michael Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan, Matthew Jackson, Samuel Coward, Jakob Foerster

ICML 2024 (Spotlight)

EvIL: Evolution Strategies for Generalisable Imitation Learning

Silvia Sapora, Chris Lu, Gokul Swamy, Yee Whye Teh, Jakob Foerster

ICML 2024

Mixture of Experts Unlock Parameter Scaling for Deep RL

Johan Obando-Ceron*, Ghada Sokar*, Timon Willi*, Clare Lyle, Jesse Farebrother, Jakob Foerster, Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

ICML 2024 (Spotlight)

Analysing the Sample Complexity of Opponent Shaping

Kitty Fung, Qizhen Zhang, Jia Wan, Timon Willi, Jakob Foerster

AAMAS 2024

Discovering Temporally-Aware Reinforcement Learning Algorithms

Matthew Jackson*, Chris Lu*, Louis Kirsch, Robert Lange, Shimon Whiteson, Jakob Foerster

ICLR 2024

Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers

Tim Franzmeyer, Stephen McAleer, Joao Henriques, Jakob Foerster, Philip Torr, Adel Bibi, Christian Schroeder de Witt

ICLR 2024

Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design

Matthew Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Greg Farquhar, Shimon Whiteson, Jakob Foerster

NeurIPS 2023

SMACv2: An Improved Benchmark for Cooperative Multi-Agent RL

Benjamin Ellis, Jonathan Cook, Skander Moalla, Mikayel Samvelyan, Mingfei Sun, Anuj Mahajan, Jakob Foerster, Shimon Whiteson

NeurIPS Datasets and Benchmarks 2023

Structured State Space Models for In-Context RL

Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob Foerster, Satinder Singh, Feryal Behbahani

NeurIPS 2023

JAX-LOB: A GPU-Accelerated Limit Order Book Simulator to Unlock Large-Scale RL for Trading

Sascha Frey*, Kang Li*, Peer Nagy*, Silvia Sapora, Chris Lu, Stefan Zohren, Jakob Foerster, Anisoara Calinescu

ICAIF 2023 (Best Academic Paper Award)

Generative AI for End-to-End Limit Order Book Modelling

Peer Nagy, Sascha Frey, Silvia Sapora, Kang Li, Anisoara Calinescu, Stefan Zohren, Jakob Foerster

ICAIF 2023

Adversarial Cheap Talk

Chris Lu, Timon Willi, Alistair Letcher, Jakob Foerster

ICML 2023

Perfectly Secure Steganography using Minimum Entropy Coupling

Christian Schroeder de Witt*, Samuel Sokota*, Ziko Kolter, Jakob Foerster, Martin Strohmeier

ICLR 2023

Adversarial Diversity for Hanabi

Brandon Cui*, Andrei Lupu*, Samuel Sokota, Hengyuan Hu, David J Wu, Jakob Foerster

ICLR 2023

Equivariant Networks for Zero-Shot Coordination

Darius Muglich, Christian Schroeder de Witt, Elise van der Pol, Jakob Foerster

NeurIPS 2022

Self-Explaining Deviations for Coordination

Hengyuan Hu, Samuel Sokota, David Wu, Anton Bakhtin, Andrei Lupu, Brandon Cui, Jakob Foerster

NeurIPS 2022

Discovered Policy Optimization

Chris Lu*, Jakub Grudzien Kuba*, Alistair Letcher, Luke Metz, Christian Schroeder de Witt, Jakob Foerster

NeurIPS 2022

Grounding Aleatoric Uncertainty for Unsupervised Environment Design

Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Kuttler, Edward Grefenstette, Tim Rocktaschel, Jakob Foerster

NeurIPS 2022

Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world

Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, Jakob Foerster

NeurIPS Datasets and Benchmarks 2022

Proximal Learning with Opponent-Learning Awareness

Stephen Zhao, Chris Lu, Roger Baker Grosse, Jakob Foerster

NeurIPS 2022

Off-Team Learning

Brandon Cui, Hengyuan Hu, Samuel Sokota, Andrei Lupu, Jakob Foerster

NeurIPS 2022

Model-Free Opponent Shaping

Chris Lu, Timon Willi, Christian Schroeder de Witt, Jakob Foerster

ICML 2022

Communicating via Markov Decision Processes

Samuel Sokota*, Christian Schroeder de Witt*, Maximilian Igl, Luisa M Zintgraf, Philip Torr, J. Zico Kolter, Shimon Whiteson, Jakob Foerster

ICML 2022

Generalized Beliefs for Cooperative AI

Darius Muglich, Luisa Zintgraf, Christian Schroeder de Witt, Shimon Whiteson, Jakob Foerster

ICML 2022

Mirror Learning: A Unifying Framework of Policy Optimisation

Jakub Grudzien Kuba, Christian Schroeder de Witt, Jakob Foerster

ICML 2022

COLA: Consistent Learning with Opponent-Learning Awareness

Timon Willi*, Johannes Treutlein*, Alistair Letcher*, Jakob Foerster

ICML 2022