Learning to Communicate with Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; Yannis M. Assael; Nando de Freitas; Shimon Whiteson (NIPS, 2016)

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks. - Jakob N. Foerster; Yannis M. Assael; Nando de Freitas; Shimon Whiteson (CoRR, 2016)

Intelligible Language Modeling with Input Switched Affine Networks. - Jakob N. Foerster; Justin Gilmer; Jan Chorowski; Jascha Sohl-Dickstein; David Sussillo (CoRR, 2016)

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. - Jakob N. Foerster; Justin Gilmer; Jascha Sohl-Dickstein; Jan Chorowski; David Sussillo (ICML, 2017)

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; Nantas Nardelli; Gregory Farquhar; Triantafyllos Afouras; Philip H. S. Torr; Pushmeet Kohli; Shimon Whiteson (ICML, 2017)

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; Nantas Nardelli; Gregory Farquhar; Philip H. S. Torr; Pushmeet Kohli; Shimon Whiteson (CoRR, 2017)

Fake News in Social Networks. - Christoph Aymanns; Jakob N. Foerster; Co-Pierre Georg (CoRR, 2017)

Counterfactual Multi-Agent Policy Gradients. - Jakob N. Foerster; Gregory Farquhar; Triantafyllos Afouras; Nantas Nardelli; Shimon Whiteson (AAAI, 2018)

Pommerman: A Multi-Agent Playground. - Cinjon Resnick; Wes Eldridge; David Ha; Denny Britz; Jakob N. Foerster; Julian Togelius; Kyunghyun Cho; Joan Bruna (AIIDE Workshops, 2018)

Learning with Opponent-Learning Awareness. - Jakob N. Foerster; Richard Y. Chen; Maruan Al-Shedivat; Shimon Whiteson; Pieter Abbeel; Igor Mordatch (AAMAS, 2018)

The Mechanics of n-Player Differentiable Games. - David Balduzzi; Sébastien Racanière; James Martens; Jakob N. Foerster; Karl Tuyls; Thore Graepel (ICML, 2018)

DiCE: The Infinitely Differentiable Monte Carlo Estimator. - Jakob N. Foerster; Gregory Farquhar; Maruan Al-Shedivat; Tim Rocktäschel; Eric P. Xing; Shimon Whiteson (ICML, 2018)

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Tabish Rashid; Mikayel Samvelyan; Christian Schröder de Witt; Gregory Farquhar; Jakob N. Foerster; Shimon Whiteson (ICML, 2018)

Deep multi-agent reinforcement learning. - Jakob N. Foerster (2018)

On the Pitfalls of Measuring Emergent Communication. - Ryan Lowe; Jakob N. Foerster; Y-Lan Boureau; Joelle Pineau; Yann N. Dauphin (AAMAS, 2019)

The StarCraft Multi-Agent Challenge. - Mikayel Samvelyan; Tabish Rashid; Christian Schröder de Witt; Gregory Farquhar; Nantas Nardelli; Tim G. J. Rudner; Chia-Man Hung; Philip H. S. Torr; Jakob N. Foerster; Shimon Whiteson (AAMAS, 2019)

Seeded self-play for language learning. - Abhinav Gupta 002; Ryan Lowe; Jakob N. Foerster; Douwe Kiela; Joelle Pineau (LANTERN@EMNLP-IJCNLP, 2019)

Stable Opponent Shaping in Differentiable Games. - Alistair Letcher; Jakob N. Foerster; David Balduzzi; Tim Rocktäschel; Shimon Whiteson (ICLR (Poster), 2019)

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; H. Francis Song; Edward Hughes 001; Neil Burch; Iain Dunning; Shimon Whiteson; Matthew M. Botvinick; Michael Bowling (ICML, 2019)

A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs. - Jingkai Mao; Jakob N. Foerster; Tim Rocktäschel; Maruan Al-Shedivat; Gregory Farquhar; Shimon Whiteson (ICML, 2019)

A Survey of Reinforcement Learning Informed by Natural Language. - Jelena Luketina; Nantas Nardelli; Gregory Farquhar; Jakob N. Foerster; Jacob Andreas; Edward Grefenstette; Shimon Whiteson; Tim Rocktäschel (IJCAI, 2019)

Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning. - Gregory Farquhar; Shimon Whiteson; Jakob N. Foerster (NeurIPS, 2019)

Multi-Agent Common Knowledge Reinforcement Learning. - Christian Schröder de Witt; Jakob N. Foerster; Gregory Farquhar; Philip H. S. Torr; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2019)

Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning. - Gregory Farquhar; Shimon Whiteson; Jakob N. Foerster (CoRR, 2019)

Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods. - Oana-Maria Camburu; Eleonora Giunchiglia; Jakob N. Foerster; Thomas Lukasiewicz; Phil Blunsom (CoRR, 2019)

Robust Domain Randomization for Reinforcement Learning. - Reda Bahi Slaoui; William R. Clements; Jakob N. Foerster; Sébastien Toth (CoRR, 2019)

Differentiable Game Mechanics. - Alistair Letcher; David Balduzzi; Sébastien Racanière; James Martens; Jakob N. Foerster; Karl Tuyls; Thore Graepel (J. Mach. Learn. Res., 2019)

Exploratory Combinatorial Optimization with Reinforcement Learning. - Thomas D. Barrett; William R. Clements; Jakob N. Foerster; A. I. Lvovsky 001 (AAAI, 2020)

Improving Policies via Search in Cooperative Partially Observable Games. - Adam Lerer; Hengyuan Hu; Jakob N. Foerster; Noam Brown (AAAI, 2020)

Capacity, Bandwidth, and Compositionality in Emergent Language Learning. - Cinjon Resnick; Abhinav Gupta 002; Jakob N. Foerster; Andrew M. Dai; Kyunghyun Cho (AAMAS, 2020)

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning. - Hengyuan Hu; Jakob N. Foerster (ICLR, 2020)

On the interaction between supervision and self-play in emergent communication. - Ryan Lowe; Abhinav Gupta 002; Jakob N. Foerster; Douwe Kiela; Joelle Pineau (ICLR, 2020)

"Other-Play" for Zero-Shot Coordination. - Hengyuan Hu; Adam Lerer; Alex Peysakhovich; Jakob N. Foerster (ICML, 2020)

Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. - Jack Parker-Holder; Luke Metz; Cinjon Resnick; Hengyuan Hu; Adam Lerer; Alistair Letcher; Alexander Peysakhovich; Aldo Pacchiano; Jakob N. Foerster (NeurIPS, 2020)

Compositionality and Capacity in Emergent Languages. - Abhinav Gupta 002; Cinjon Resnick; Jakob N. Foerster; Andrew M. Dai; Kyunghyun Cho (RepL4NLP@ACL, 2020)

The Hanabi challenge: A new frontier for AI research. - Nolan Bard; Jakob N. Foerster; Sarath Chandar; Neil Burch; Marc Lanctot; H. Francis Song; Emilio Parisotto; Vincent Dumoulin; Subhodeep Moitra; Edward Hughes 001; Iain Dunning; Shibl Mourad; et al. (Artif. Intell., 2020)

The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets. - Oana-Maria Camburu; Eleonora Giunchiglia; Jakob N. Foerster; Thomas Lukasiewicz; Phil Blunsom (CoRR, 2020)

Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations. - Kalesha Bullard; Franziska Meier; Douwe Kiela; Joelle Pineau; Jakob N. Foerster (CoRR, 2020)

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Tabish Rashid; Mikayel Samvelyan; Christian Schröder de Witt; Gregory Farquhar; Jakob N. Foerster; Shimon Whiteson (J. Mach. Learn. Res., 2020)

Trajectory Diversity for Zero-Shot Coordination. - Andrei Lupu; Hengyuan Hu; Jakob N. Foerster (AAMAS, 2021)

Off-Belief Learning. - Hengyuan Hu; Adam Lerer; Brandon Cui; Luis Pineda; Noam Brown; Jakob N. Foerster (ICML, 2021)

Trajectory Diversity for Zero-Shot Coordination. - Andrei Lupu; Brandon Cui; Hengyuan Hu; Jakob N. Foerster (ICML, 2021)

A New Formalism, Method and Open Issues for Zero-Shot Coordination. - Johannes Treutlein; Michael Dennis 001; Caspar Oesterheld; Jakob N. Foerster (ICML, 2021)

K-level Reasoning for Zero-Shot Coordination in Hanabi. - Brandon Cui; Hengyuan Hu; Luis Pineda; Jakob N. Foerster (NeurIPS, 2021)

Replay-Guided Adversarial Environment Design. - Minqi Jiang; Michael Dennis 001; Jack Parker-Holder; Jakob N. Foerster; Edward Grefenstette; Tim Rocktäschel (NeurIPS, 2021)

Neural Pseudo-Label Optimism for the Bank Loan Problem. - Aldo Pacchiano; Shaun Singh; Edward Chou; Alexander C. Berg; Jakob N. Foerster (NeurIPS, 2021)

Off-Belief Learning. - Hengyuan Hu; Adam Lerer; Brandon Cui; Luis Pineda; David J. Wu 002; Noam Brown; Jakob N. Foerster (CoRR, 2021)

Quasi-Equivalence Discovery for Zero-Shot Emergent Communication. - Kalesha Bullard; Douwe Kiela; Joelle Pineau; Jakob N. Foerster (CoRR, 2021)

Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings. - Hengyuan Hu; Adam Lerer; Noam Brown; Jakob N. Foerster (CoRR, 2021)

Implicit Communication as Minimum Entropy Coupling. - Samuel Sokota; Christian Schröder de Witt; Maximilian Igl; Luisa M. Zintgraf; Philip H. S. Torr; Shimon Whiteson; Jakob N. Foerster (CoRR, 2021)

Don't Sweep your Learning Rate under the Rug: A Closer Look at Cross-modal Transfer of Pretrained Transformers. - Danielle Rothermel; Margaret Li; Tim Rocktäschel; Jakob N. Foerster (CoRR, 2021)

Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization. - Dmitrii Beloborodov; Alexander E. Ulanov; Jakob N. Foerster; Shimon Whiteson; A. I. Lvovsky 001 (Mach. Learn. Sci. Technol., 2021)

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

Lyapunov Exponents for Diversity in Differentiable Games. - Jonathan Lorraine; Paul Vicol; Jack Parker-Holder; Tal Kachman; Luke Metz; Jakob N. Foerster (AAMAS, 2022)

A Fine-Tuning Approach to Belief State Modeling. - Samuel Sokota; Hengyuan Hu; David J. Wu 002; J. Zico Kolter; Jakob Nicolaus Foerster; Noam Brown (ICLR, 2022)

Mirror Learning: A Unifying Framework of Policy Optimisation. - Jakub Grudzien Kuba; Christian A. Schröder de Witt; Jakob N. Foerster (ICML, 2022)

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

Generalized Beliefs for Cooperative AI. - Darius Muglich; Luisa M. Zintgraf; Christian A. Schröder de Witt; Shimon Whiteson; Jakob N. Foerster (ICML, 2022)

Evolving Curricula with Regret-Based Environment Design. - Jack Parker-Holder; Minqi Jiang; Michael Dennis 001; Mikayel Samvelyan; Jakob N. Foerster; Edward Grefenstette; Tim Rocktäschel (ICML, 2022)

Communicating via Markov Decision Processes. - Samuel Sokota; Christian A. Schröder de Witt; Maximilian Igl; Luisa M. Zintgraf; Philip H. S. Torr; Martin Strohmeier; J. Zico Kolter; Shimon Whiteson; Jakob N. Foerster (ICML, 2022)

COLA: Consistent Learning with Opponent-Learning Awareness. - Timon Willi; Alistair Letcher; Johannes Treutlein; 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)

Off-Team Learning. - Brandon Cui; Hengyuan Hu; Andrei Lupu; Samuel Sokota; Jakob N. Foerster (NeurIPS, 2022)

Self-Explaining Deviations for Coordination. - Hengyuan Hu; Samuel Sokota; David J. Wu 002; Anton Bakhtin; Andrei Lupu; Brandon Cui; Jakob N. Foerster (NeurIPS, 2022)

Grounding Aleatoric Uncertainty for Unsupervised Environment Design. - Minqi Jiang; Michael Dennis 001; Jack Parker-Holder; Andrei Lupu; Heinrich Küttler; Edward Grefenstette; Tim Rocktäschel; Jakob N. Foerster (NeurIPS, 2022)

Influencing Long-Term Behavior in Multiagent Reinforcement Learning. - Dong-Ki Kim; Matthew Riemer; Miao Liu 001; Jakob N. Foerster; Michael Everett; Chuangchuang Sun; Gerald Tesauro; Jonathan P. How (NeurIPS, 2022)

Equivariant Networks for Zero-Shot Coordination. - Darius Muglich; Christian Schröder de Witt; Elise van der Pol; Shimon Whiteson; Jakob N. 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 N. Foerster (NeurIPS, 2022)

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

Learning to Coordinate with Humans using Action Features. - Mingwei Ma; Jizhou Liu; Samuel Sokota; Max Kleiman-Weiner; Jakob N. Foerster (CoRR, 2022)

Illusionary Attacks on Sequential Decision Makers and Countermeasures. - Tim Franzmeyer; João F. Henriques; Jakob N. Foerster; Philip H. S. Torr; Adel Bibi; Christian Schröder de Witt (CoRR, 2022)

An Investigation of the Bias-Variance Tradeoff in Meta-Gradients. - Risto Vuorio; Jacob Beck; Shimon Whiteson; Jakob N. Foerster; Gregory Farquhar (CoRR, 2022)

Human-AI Coordination via Human-Regularized Search and Learning. - Hengyuan Hu; David J. Wu 002; Adam Lerer; Jakob N. Foerster; Noam Brown (CoRR, 2022)

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria. - Dong-Ki Kim; Matthew Riemer; Miao Liu 001; Jakob N. Foerster; Gerald Tesauro; Jonathan P. How (CoRR, 2022)

Similarity-based Cooperation. - Caspar Oesterheld; Johannes Treutlein; Roger B. Grosse; Vincent Conitzer; Jakob N. Foerster (CoRR, 2022)

SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning. - Benjamin Ellis; Skander Moalla; Mikayel Samvelyan; Mingfei Sun 001; Anuj Mahajan; Jakob N. Foerster; Shimon Whiteson (CoRR, 2022)

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)

Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network. - Peer Nagy; Sascha Frey; Silvia Sapora; Kang Li; Anisoara Calinescu; Stefan Zohren; Jakob N. Foerster (ICAIF, 2023)

Adversarial Diversity in Hanabi. - Brandon Cui; Andrei Lupu; Samuel Sokota; Hengyuan Hu; David J. Wu 002; Jakob Nicolaus Foerster (ICLR, 2023)

Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning. - Yat Long Lo; Christian Schröder de Witt; Samuel Sokota; Jakob Nicolaus Foerster; Shimon Whiteson (ICLR, 2023)

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. - Mikayel Samvelyan; Akbir Khan; Michael Dennis 001; Minqi Jiang; Jack Parker-Holder; Jakob Nicolaus Foerster; Roberta Raileanu; Tim Rocktäschel (ICLR, 2023)

Perfectly Secure Steganography Using Minimum Entropy Coupling. - Christian Schröder de Witt; Samuel Sokota; J. Zico Kolter; Jakob Nicolaus Foerster; Martin Strohmeier (ICLR, 2023)

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

Learning Intuitive Policies Using Action Features. - Mingwei Ma; Jizhou Liu; Samuel Sokota; Max Kleiman-Weiner; 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)

SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning. - Benjamin Ellis; Jonathan Cook 004; Skander Moalla; Mikayel Samvelyan; Mingfei Sun 001; Anuj Mahajan; Jakob N. Foerster; Shimon Whiteson (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)

Similarity-based cooperative equilibrium. - Caspar Oesterheld; Johannes Treutlein; Roger B. Grosse; Vincent Conitzer; 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)

Learning to Communicate using Contrastive Learning. - Yat Long Lo; Biswa Sengupta; Jakob N. Foerster; Michael Noukhovitch (CoRR, 2023)

Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem. - Elena Gal; Shaun Singh; Aldo Pacchiano; Ben Walker; Terry J. Lyons; Jakob N. Foerster (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)

Learning to Optimize Quasi-Newton Methods. - Isaac Liao; Rumen Dangovski; Jakob Nicolaus Foerster; Marin Soljacic (Trans. Mach. Learn. Res., 2023)

HelloFresh: LLM Evalutions on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits. - Tim Franzmeyer; Aleksandar Shtedritski; Samuel Albanie; Philip Torr 001; João F. Henriques; Jakob N. Foerster (ACL (Findings), 2024)

A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem. - Paul Barde; Jakob N. Foerster; Derek Nowrouzezahrai; Amy Zhang 001 (AAMAS, 2024)

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)

Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection. - Linas Nasvytis; Kai Sandbrink; Jakob N. Foerster; Tim Franzmeyer; Christian Schröder de Witt (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)

Select to Perfect: Imitating desired behavior from large multi-agent data. - Tim Franzmeyer; Edith Elkind; Philip Torr 001; Jakob Nicolaus Foerster; João F. Henriques (ICLR, 2024)

Illusory Attacks: Information-theoretic detectability matters in adversarial attacks. - Tim Franzmeyer; Stephen Marcus McAleer; João F. Henriques; Jakob Nicolaus Foerster; Philip Torr 001; Adel Bibi; Christian Schröder de Witt (ICLR, 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)

Learning Multi-Agent Communication with Contrastive Learning. - Yat Long Lo; Biswa Sengupta; Jakob Nicolaus Foerster; Michael Noukhovitch (ICLR, 2024)

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

Refining Minimax Regret for Unsupervised Environment Design. - Michael Beukman; Samuel Coward; Michael T. Matthews; Mattie Fellows; Minqi Jiang; Michael D. Dennis; Jakob Nicolaus Foerster (ICML, 2024)

Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI. - Francisco Eiras; Aleksandar Petrov; Bertie Vidgen; Christian Schröder de Witt; Fabio Pizzati; Katherine Elkins; Supratik Mukhopadhyay; Adel Bibi; Botos Csaba; Fabro Steibel; Fazl Barez; Genevieve Smith; et al. (ICML, 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)

Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning. - Michael T. Matthews; Michael Beukman; Benjamin Ellis; Mikayel Samvelyan; Matthew Thomas Jackson; Samuel Coward; Jakob Nicolaus Foerster (ICML, 2024)

Mixtures of Experts Unlock Parameter Scaling for Deep RL. - Johan S. Obando-Ceron; Ghada Sokar; Timon Willi; Clare Lyle; Jesse Farebrother; Jakob Nicolaus Foerster; Gintare Karolina Dziugaite; Doina Precup; Pablo Samuel Castro (ICML, 2024)

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

PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition. - Ziyang Zhang; Qizhen Zhang 002; 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)

Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps. - Benjamin Ellis; Matthew Thomas Jackson; Andrei Lupu; Alexander David Goldie; Mattie Fellows; Shimon Whiteson; 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)

No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery. - Alexander Rutherford; Michael Beukman; Timon Willi; Bruno Lacerda; Nick Hawes; Jakob N. Foerster (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)

Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts. - Mikayel Samvelyan; Sharath Chandra Raparthy; Andrei Lupu; Eric Hambro; Aram H. Markosyan; Manish Bhatt; Yuning Mao; Minqi Jiang; Jack Parker-Holder; Jakob N. Foerster; Tim Rocktäschel; Roberta Raileanu (NeurIPS, 2024)

The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning. - Anya Sims; Cong Lu; Jakob N. Foerster; Yee Whye Teh (NeurIPS, 2024)

Melting Pot Contest: Charting the Future of Generalized Cooperative Intelligence. - Rakshit Trivedi; Akbir Khan; Jesse Clifton; Lewis Hammond; Edgar A. Duéñez-Guzmán; Dipam Chakraborty; John P. Agapiou; Jayd Matyas; Alexander Sasha Vezhnevets; Barna Pásztor; Yunke Ao; Omar G. Younis; et al. (NeurIPS, 2024)

BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts. - Qizhen (Irene) Zhang; Nikolas Gritsch; Dwaraknath Gnaneshwar; Simon Guo 003; David Cairuz; Bharat Venkitesh; Jakob N. Foerster; Phil Blunsom; Sebastian Ruder; Ahmet Üstün; Acyr Locatelli (NeurIPS, 2024)

Policy-Guided Diffusion. - Matthew Thomas Jackson; Michael T. Matthews; Cong Lu; Benjamin Ellis; Shimon Whiteson; Jakob Nicolaus Foerster (RLJ, 2024)

Mixture of Experts in a Mixture of RL settings. - Timon Willi; Johan S. Obando-Ceron; Jakob Nicolaus Foerster; Gintare Karolina Dziugaite; Pablo Samuel Castro (RLJ, 2024)

Reinforcement Learning Controllers for Soft Robots Using Learned Environments. - Uljad Berdica; Matthew Thomas Jackson; Niccolò Enrico Veronese; Jakob N. Foerster; Perla Maiolino (RoboSoft, 2024)

Computing Low-Entropy Couplings for Large-Support Distributions. - Samuel Sokota; Dylan Sam; Christian Schröder de Witt; Spencer Compton; Jakob N. Foerster; J. Zico Kolter (UAI, 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)

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

Symmetry-Breaking Augmentations for Ad Hoc Teamwork. - Ravi Hammond; Dustin Craggs; Mingyu Guo; Jakob N. Foerster; Ian D. Reid 001 (CoRR, 2024)

JaxUED: A simple and useable UED library in Jax. - Samuel Coward; Michael Beukman; Jakob N. Foerster (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (CoRR, 2024)

Near to Mid-term Risks and Opportunities of Open Source Generative AI. - Francisco Eiras; Aleksandar Petrov; Bertie Vidgen; Christian Schröder de Witt; Fabio Pizzati; Katherine Elkins; Supratik Mukhopadhyay; Adel Bibi; Botos Csaba; Fabro Steibel; Fazl Barez; Genevieve Smith; et al. (CoRR, 2024)

Risks and Opportunities of Open-Source Generative AI. - Francisco Eiras; Aleksandar Petrov; Bertie Vidgen; Christian Schröder de Witt; Fabio Pizzati; Katherine Elkins; Supratik Mukhopadhyay; Adel Bibi; Aaron Purewal; Botos Csaba; Fabro Steibel; Fazel Keshtkar; et al. (CoRR, 2024)

HelloFresh: LLM Evaluations on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits. - Tim Franzmeyer; Aleksandar Shtedritski; Samuel Albanie; Philip Torr 001; João F. Henriques; Jakob N. Foerster (CoRR, 2024)

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

Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources. - Alisia Maria Lupidi; Carlos Gemmell; Nicola Cancedda; Jane Dwivedi-Yu; Jason Weston; Jakob N. Foerster; Roberta Raileanu; Maria Lomeli (CoRR, 2024)

Opponent Shaping for Antibody Development. - Sebastian Towers; Aleksandra Kalisz; Philippe A. Robert; Alicia Higueruelo; Francesca V. Vianello; Ming-Han Chloe Tsai; Harrison Steel; Jakob N. Foerster (CoRR, 2024)

TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation. - Jonathan Cook 004; Tim Rocktäschel; Jakob N. Foerster; Dennis Aumiller; Alex Wang (CoRR, 2024)

CURATe: Benchmarking Personalised Alignment of Conversational AI Assistants. - Lize Alberts; Benjamin Ellis; Andrei Lupu; Jakob N. Foerster (CoRR, 2024)

Beyond the Boundaries of Proximal Policy Optimization. - Charlie B. Tan; Edan Toledo; Benjamin Ellis; Jakob N. Foerster; Ferenc Huszár (CoRR, 2024)

Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination Games. - Usman Anwar; Ashish Pandian; Jia Wan; David Krueger 001; Jakob N. Foerster (CoRR, 2024)

Learning Loss Landscapes in Preference Optimization. - Carlo Alfano; Silvia Sapora; Jakob Nicolaus Foerster; Patrick Rebeschini; Yee Whye Teh (CoRR, 2024)

The Complexity Dynamics of Grokking. - Branton DeMoss; Silvia Sapora; Jakob N. Foerster; Nick Hawes; Ingmar Posner (CoRR, 2024)

Optimisation strategies for directed evolution without sequencing. - Jessica James; Sebastian Towers; Jakob N. Foerster; Harrison Steel (PLoS Comput. Biol., 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (Trans. Mach. Learn. Res., 2024)

Simplifying Deep Temporal Difference Learning. - Matteo Gallici; Mattie Fellows; Benjamin Ellis; Bartomeu Pou; Ivan Masmitja; Jakob Nicolaus Foerster; Mario Martin (ICLR, 2025)

OvercookedV2: Rethinking Overcooked for Zero-Shot Coordination. - Tobias Gessler; Tin Dizdarevic; Ani Calinescu; Benjamin Ellis; Andrei Lupu; Jakob Nicolaus Foerster (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)

Expected Return Symmetries. - Darius Muglich; Johannes Forkel; Elise van der Pol; Jakob Nicolaus Foerster (ICLR, 2025)

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games. - Davide Paglieri; Bartlomiej Cupial; Samuel Coward; Ulyana Piterbarg; Maciej Wolczyk; Akbir Khan; Eduardo Pignatelli; Lukasz Kucinski; Lerrel Pinto; Rob Fergus; Jakob Nicolaus Foerster; Jack Parker-Holder; et al. (ICLR, 2025)

BTS: Harmonizing Specialized Experts into a Generalist LLM. - Qizhen Zhang 002; Prajjwal Bhargava; Chloe Bi; Chris X. Cai; Jakob N. Foerster; Jeremy Fu; Punit Singh Koura; Ruan Silva; Sheng Shen 016; Emily Dinan; Suchin Gururangan; Mike Lewis (CoRR, 2025)

AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds. - J. Rosser; Jakob Nicolaus Foerster (CoRR, 2025)

LOB-Bench: Benchmarking Generative AI for Finance - an Application to Limit Order Book Data. - Peer Nagy; Sascha Frey; Kang Li; Bidipta Sarkar; Svitlana Vyetrenko; Stefan Zohren; Ani Calinescu; Jakob N. Foerster (CoRR, 2025)

Learning to Reason at the Frontier of Learnability. - Thomas Foster; Jakob N. Foerster (CoRR, 2025)

Multi-Agent Risks from Advanced AI. - Lewis Hammond; Alan Chan; Jesse Clifton; Jason Hoelscher-Obermaier; Akbir Khan; Euan McLean; Chandler Smith; Wolfram Barfuss; Jakob N. Foerster; Tomas Gavenciak; The Anh Han; Edward Hughes 001; et al. (CoRR, 2025)

MLGym: A New Framework and Benchmark for Advancing AI Research Agents. - Deepak Nathani; Lovish Madaan; Nicholas Roberts; Nikolay Bashlykov; Ajay Menon; Vincent Moens; Amar Budhiraja; Despoina Magka; Vladislav Vorotilov; Gaurav Chaurasia; Dieuwke Hupkes; Ricardo Silveira Cabral; et al. (CoRR, 2025)

Meta-learning characteristics and dynamics of quantum systems. - Lucas Schorling; Pranav Vaidhyanathan; Jonas Schuff; Miguel J. Carballido; Dominik M. Zumbühl; Gerard Milburn; Florian Marquardt; Jakob N. Foerster; Michael A. Osborne; Natalia Ares (CoRR, 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)

A Clean Slate for Offline Reinforcement Learning. - Matthew Thomas Jackson; Uljad Berdica; Jarek Liesen; Shimon Whiteson; Jakob Nicolaus Foerster (CoRR, 2025)

An Optimisation Framework for Unsupervised Environment Design. - Nathan Monette; Alistair Letcher; Michael Beukman; Matthew Thomas Jackson; Alexander Rutherford; Alexander David Goldie; Jakob N. Foerster (CoRR, 2025)

SOReL and TOReL: Two Methods for Fully Offline Reinforcement Learning. - Mattie Fellows; Clarisse Wibault; Uljad Berdica; Johannes Forkel; Michael A. Osborne; Jakob N. Foerster (CoRR, 2025)

StochasTok: Improving Fine-Grained Subword Understanding in LLMs. - Anya Sims; Thom Foster; Klara Kaleb; Tuan-Duy H. Nguyen; Joseph Lee; Jakob N. Foerster; Yee Whye Teh; Cong Lu (CoRR, 2025)

High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning. - Tim Franzmeyer; Archie Sravankumar; Lijuan Liu; Yuning Mao; Rui Hou; Sinong Wang; Jakob N. Foerster; Luke Zettlemoyer; Madian Khabsa (CoRR, 2025)

Intent Factored Generation: Unleashing the Diversity in Your Language Model. - Eltayeb Ahmed; Uljad Berdica; Martha Elliott; Danijela Horak; Jakob N. Foerster (CoRR, 2025)

Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training. - Jonathan Cook 004; Silvia Sapora; Arash Ahmadian; Akbir Khan; Tim Rocktäschel; Jakob N. Foerster; Laura Ruis (CoRR, 2025)

The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind. - Andrei Lupu; Timon Willi; Jakob N. Foerster (CoRR, 2025)

Ad-Hoc Human-AI Coordination Challenge. - Tin Dizdarevic; Ravi Hammond; Tobias Gessler; Anisoara Calinescu; Jonathan Cook 004; Matteo Gallici; Andrei Lupu; Darius Muglich; Johannes Forkel; Jakob Nicolaus Foerster (CoRR, 2025)

The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements. - Bingchen Zhao; Despoina Magka; Minqi Jiang; Xian Li; Roberta Raileanu; Tatiana Shavrina; Jean-Christophe Gagnon-Audet; Kelvin Niu; Shagun Sodhani; Michael Shvartsman; Andrei Lupu; Alisia Maria Lupidi; et al. (CoRR, 2025)

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench. - Edan Toledo; Karen Hambardzumyan; Martin Josifoski; Rishi Hazra; Nicolas Mario Baldwin; Alexis Audran-Reiss; Michael Kuchnik; Despoina Magka; Minqi Jiang; Alisia Maria Lupidi; Andrei Lupu; Roberta Raileanu; et al. (CoRR, 2025)

How Should We Meta-Learn Reinforcement Learning Algorithms? - Alexander David Goldie; Zilin Wang; Jakob Nicolaus Foerster; Shimon Whiteson (CoRR, 2025)