Jakob N. Foerster

ORCID

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 149

Top Venues

Publications

Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Garðar Ingvarsson, Timon Willi, Akbir Khan, Christian Schröder de Witt, Alexandra Souly, Saptarashmi Bandyopadhyay, Mikayel Samvelyan, Minqi Jiang, Robert T. Lange, Shimon Whiteson, Bruno Lacerda, Nick Hawes, Tim Rocktäschel, Chris Lu, Jakob N. Foerster
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX. AAMAS (2024)
Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob N. Foerster
Analysing the Sample Complexity of Opponent Shaping. AAMAS (2024)
Tim Franzmeyer, Aleksandar Shtedritski, Samuel Albanie, Philip Torr, João F. Henriques, Jakob N. Foerster
HelloFresh: LLM Evalutions on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits. ACL (Findings) (2024)
Timon Willi, Johan S. Obando-Ceron, Jakob N. Foerster, Karolina Dziugaite, Pablo Samuel Castro
Mixture of Experts in a Mixture of RL settings. CoRR (2024)
Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Scaling Opponent Shaping to High Dimensional Games. AAMAS (2024)
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
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts. CoRR (2024)
Andrei Lupu, Chris Lu, Jarek Liesen, Robert Tjarko Lange, Jakob N. Foerster
Behaviour Distillation. CoRR (2024)
Jarek Liesen, Chris Lu, Andrei Lupu, Jakob N. Foerster, Henning Sprekeler, Robert T. Lange
Discovering Minimal Reinforcement Learning Environments. CoRR (2024)
Jonathan Cook, Chris Lu, Edward Hughes, Joel Z. Leibo, Jakob N. Foerster
Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning. CoRR (2024)
Tim Franzmeyer, Edith Elkind, Philip H. S. Torr, Jakob N. Foerster, João F. Henriques
Select to Perfect: Imitating desired behavior from large multi-agent data. CoRR (2024)
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, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan Arturo Nolazco, Lori Landay, Matthew Jackson, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob N. Foerster
Risks and Opportunities of Open-Source Generative AI. CoRR (2024)
Steven D. Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob N. Foerster, Amanda Prorok
Revisiting Recurrent Reinforcement Learning with Memory Monoids. CoRR (2024)
Tim Franzmeyer, Aleksandar Shtedritski, Samuel Albanie, Philip Torr, João F. Henriques, Jakob N. Foerster
HelloFresh: LLM Evaluations on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits. CoRR (2024)
Samuel Sokota, Dylan Sam, Christian Schröder de Witt, Spencer Compton, Jakob N. Foerster, J. Zico Kolter
Computing Low-Entropy Couplings for Large-Support Distributions. CoRR (2024)
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, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger
Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR (2024)
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, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Jackson, Paul Röttger, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob N. Foerster
Near to Mid-term Risks and Opportunities of Open Source Generative AI. CoRR (2024)
Matthew Thomas Jackson, Michael T. Matthews, Cong Lu, Benjamin Ellis, Shimon Whiteson, Jakob N. Foerster
Policy-Guided Diffusion. CoRR (2024)
Uljad Berdica, Matthew Jackson, Niccolò Enrico Veronese, Jakob N. Foerster, Perla Maiolino
Reinforcement Learning Controllers for Soft Robots Using Learned Environments. RoboSoft (2024)
Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob N. Foerster
Analysing the Sample Complexity of Opponent Shaping. CoRR (2024)
Michael Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan, Matthew Thomas Jackson, Samuel Coward, Jakob N. Foerster
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning. CoRR (2024)
Linas Nasvytis, Kai Sandbrink, Jakob N. Foerster, Tim Franzmeyer, Christian Schröder de Witt
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection. AAMAS (2024)
Linas Nasvytis, Kai Sandbrink, Jakob N. Foerster, Tim Franzmeyer, Christian Schröder de Witt
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection. CoRR (2024)
Jake Levi, Chris Lu, Timon Willi, Christian Schröder de Witt, Jakob N. Foerster
The Danger Of Arrogance: Welfare Equilibra As A Solution To Stackelberg Self-Play In Non-Coincidental Games. CoRR (2024)
Chris Lu, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob N. Foerster, Mihaela van der Schaar, Robert Tjarko Lange
Discovering Preference Optimization Algorithms with and for Large Language Models. CoRR (2024)
Michael Beukman, Samuel Coward, Michael Matthews, Mattie Fellows, Minqi Jiang, Michael Dennis, Jakob N. Foerster
Refining Minimax Regret for Unsupervised Environment Design. CoRR (2024)
Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob N. Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro
Mixtures of Experts Unlock Parameter Scaling for Deep RL. CoRR (2024)
Samuel Coward, Michael Beukman, Jakob N. Foerster
JaxUED: A simple and useable UED library in Jax. CoRR (2024)
Yat Long Lo, Biswa Sengupta, Jakob N. Foerster, Michael Noukhovitch
Learning to Communicate using Contrastive Learning. CoRR (2023)
Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Gregory Farquhar, Shimon Whiteson, Jakob N. Foerster
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design. NeurIPS (2023)
Sascha Yves Frey, Kang Li, Peer Nagy, Silvia Sapora, Christopher Lu, Stefan Zohren, Jakob N. Foerster, Anisoara Calinescu
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading. ICAIF (2023)
Elena Gal, Shaun Singh, Aldo Pacchiano, Ben Walker, Terry Lyons, Jakob N. Foerster
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem. CoRR (2023)
Chris Lu, Sebastian Towers, Jakob N. Foerster
Arbitrary Order Meta-Learning with Simple Population-Based Evolution. CoRR (2023)
Benjamin Ellis, Jonathan Cook, Skander Moalla, Mikayel Samvelyan, Mingfei Sun, Anuj Mahajan, Jakob N. Foerster, Shimon Whiteson
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning. NeurIPS (2023)
Mikayel Samvelyan, Akbir Khan, Michael Dennis, Minqi Jiang, Jack Parker-Holder, Jakob N. Foerster, Roberta Raileanu, Tim Rocktäschel
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. CoRR (2023)
Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Scaling Opponent Shaping to High Dimensional Games. CoRR (2023)
Peer Nagy, Sascha Frey, Silvia Sapora, Kang Li, Anisoara Calinescu, Stefan Zohren, Jakob N. Foerster
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. CoRR (2023)
Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster
Similarity-based cooperative equilibrium. NeurIPS (2023)
Sascha Frey, Kang Li, Peer Nagy, Silvia Sapora, Chris Lu, Stefan Zohren, Jakob N. Foerster, Anisoara Calinescu
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading. CoRR (2023)
Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob N. Foerster, Satinder Singh, Feryal M. P. Behbahani
Structured State Space Models for In-Context Reinforcement Learning. NeurIPS (2023)
Peer Nagy, Sascha Frey, Silvia Sapora, Kang Li, Anisoara Calinescu, Stefan Zohren, Jakob N. Foerster
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. ICAIF (2023)
Jakub Grudzien Kuba, Christian Schröder de Witt, Jakob N. Foerster
Mirror Learning: A Unifying Framework of Policy Optimisation. CoRR (2022)
Christopher Lu, Timon Willi, Christian A. Schröder de Witt, Jakob N. Foerster
Model-Free Opponent Shaping. ICML (2022)
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
Communicating via Markov Decision Processes. ICML (2022)
Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How
Influencing Long-Term Behavior in Multiagent Reinforcement Learning. NeurIPS (2022)
Darius Muglich, Luisa M. Zintgraf, Christian Schröder de Witt, Shimon Whiteson, Jakob N. Foerster
Generalized Beliefs for Cooperative AI. CoRR (2022)
Darius Muglich, Christian Schröder de Witt, Elise van der Pol, Shimon Whiteson, Jakob N. Foerster
Equivariant Networks for Zero-Shot Coordination. NeurIPS (2022)
Hengyuan Hu, Samuel Sokota, David J. Wu, Anton Bakhtin, Andrei Lupu, Brandon Cui, Jakob N. Foerster
Self-Explaining Deviations for Coordination. CoRR (2022)
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
Evolving Curricula with Regret-Based Environment Design. ICML (2022)
Stephen Zhao, Chris Lu, Roger B. Grosse, Jakob N. Foerster
Proximal Learning With Opponent-Learning Awareness. NeurIPS (2022)
Tim Franzmeyer, João F. Henriques, Jakob N. Foerster, Philip H. S. Torr, Adel Bibi, Christian Schröder de Witt
Illusionary Attacks on Sequential Decision Makers and Countermeasures. CoRR (2022)
Chris Lu, Timon Willi, Alistair Letcher, Jakob N. Foerster
Adversarial Cheap Talk. CoRR (2022)
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob N. Foerster
Lyapunov Exponents for Diversity in Differentiable Games. AAMAS (2022)
Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schröder de Witt, Jakob N. Foerster
Discovered Policy Optimisation. CoRR (2022)
Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster
Similarity-based Cooperation. CoRR (2022)
Darius Muglich, Luisa M. Zintgraf, Christian A. Schröder de Witt, Shimon Whiteson, Jakob N. Foerster
Generalized Beliefs for Cooperative AI. ICML (2022)
Hengyuan Hu, David J. Wu, Adam Lerer, Jakob N. Foerster, Noam Brown
Human-AI Coordination via Human-Regularized Search and Learning. CoRR (2022)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Grounding Aleatoric Uncertainty for Unsupervised Environment Design. NeurIPS (2022)
Darius Muglich, Christian Schröder de Witt, Elise van der Pol, Shimon Whiteson, Jakob N. Foerster
Equivariant Networks for Zero-Shot Coordination. CoRR (2022)
Brandon Cui, Hengyuan Hu, Andrei Lupu, Samuel Sokota, Jakob N. Foerster
Off-Team Learning. NeurIPS (2022)
Timon Willi, Alistair Letcher, Johannes Treutlein, Jakob N. Foerster
COLA: Consistent Learning with Opponent-Learning Awareness. ICML (2022)
Hengyuan Hu, Samuel Sokota, David J. Wu, Anton Bakhtin, Andrei Lupu, Brandon Cui, Jakob N. Foerster
Self-Explaining Deviations for Coordination. NeurIPS (2022)
Timon Willi, Johannes Treutlein, Alistair Letcher, Jakob N. Foerster
COLA: Consistent Learning with Opponent-Learning Awareness. CoRR (2022)
Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob N. Foerster
Learning to Coordinate with Humans using Action Features. CoRR (2022)
Benjamin Ellis, Skander Moalla, Mikayel Samvelyan, Mingfei Sun, Anuj Mahajan, Jakob N. Foerster, Shimon Whiteson
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning. CoRR (2022)
Risto Vuorio, Jacob Beck, Shimon Whiteson, Jakob N. Foerster, Gregory Farquhar
An Investigation of the Bias-Variance Tradeoff in Meta-Gradients. CoRR (2022)
Qizhen Zhang, Christopher Lu, Animesh Garg, Jakob N. Foerster
Centralized Model and Exploration Policy for Multi-Agent RL. AAMAS (2022)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Grounding Aleatoric Uncertainty in Unsupervised Environment Design. CoRR (2022)
Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How
Influencing Long-Term Behavior in Multiagent Reinforcement Learning. CoRR (2022)
Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Gerald Tesauro, Jonathan P. How
Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria. CoRR (2022)
Jakub Grudzien Kuba, Christian A. Schröder de Witt, Jakob N. Foerster
Mirror Learning: A Unifying Framework of Policy Optimisation. ICML (2022)
Christopher Lu, Timon Willi, Christian Schröder de Witt, Jakob N. Foerster
Model-Free Opponent Shaping. CoRR (2022)
Isaac Liao, Rumen R. Dangovski, Jakob N. Foerster, Marin Soljacic
Learning to Optimize Quasi-Newton Methods. CoRR (2022)
Christian Schröder de Witt, Samuel Sokota, J. Zico Kolter, Jakob N. Foerster, Martin Strohmeier
Perfectly Secure Steganography Using Minimum Entropy Coupling. CoRR (2022)
Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schröder de Witt, Jakob N. Foerster
Discovered Policy Optimisation. NeurIPS (2022)
Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob N. Foerster
K-level Reasoning for Zero-Shot Coordination in Hanabi. CoRR (2022)
Danielle Rothermel, Margaret Li, Tim Rocktäschel, Jakob N. Foerster
Don't Sweep your Learning Rate under the Rug: A Closer Look at Cross-modal Transfer of Pretrained Transformers. CoRR (2021)
Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, Noam Brown, Jakob N. Foerster
Off-Belief Learning. ICML (2021)
Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster
Neural Pseudo-Label Optimism for the Bank Loan Problem. CoRR (2021)
Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, David Wu, Noam Brown, Jakob N. Foerster
Off-Belief Learning. CoRR (2021)
Dmitrii Beloborodov, Alexander E. Ulanov, Jakob N. Foerster, Shimon Whiteson, A. I. Lvovsky
Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization. Mach. Learn. Sci. Technol. 2 (2) (2021)
Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob N. Foerster
K-level Reasoning for Zero-Shot Coordination in Hanabi. NeurIPS (2021)
Andrei Lupu, Brandon Cui, Hengyuan Hu, Jakob N. Foerster
Trajectory Diversity for Zero-Shot Coordination. ICML (2021)
Hengyuan Hu, Adam Lerer, Noam Brown, Jakob N. Foerster
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings. CoRR (2021)
Samuel Sokota, Christian Schröder de Witt, Maximilian Igl, Luisa M. Zintgraf, Philip H. S. Torr, Shimon Whiteson, Jakob N. Foerster
Implicit Communication as Minimum Entropy Coupling. CoRR (2021)
Andrei Lupu, Hengyuan Hu, Jakob N. Foerster
Trajectory Diversity for Zero-Shot Coordination. AAMAS (2021)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
Replay-Guided Adversarial Environment Design. NeurIPS (2021)
Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster
Neural Pseudo-Label Optimism for the Bank Loan Problem. NeurIPS (2021)
Kalesha Bullard, Douwe Kiela, Joelle Pineau, Jakob N. Foerster
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication. CoRR (2021)
Adam Lerer, Hengyuan Hu, Jakob N. Foerster, Noam Brown
Improving Policies via Search in Cooperative Partially Observable Games. AAAI (2020)
Abhinav Gupta, Cinjon Resnick, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho
Compositionality and Capacity in Emergent Languages. RepL4NLP@ACL (2020)
Ryan Lowe, Abhinav Gupta, Jakob N. Foerster, Douwe Kiela, Joelle Pineau
On the interaction between supervision and self-play in emergent communication. CoRR (2020)
Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob N. Foerster
"Other-Play" for Zero-Shot Coordination. CoRR (2020)
Hengyuan Hu, Jakob N. Foerster
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning. ICLR (2020)
Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob N. Foerster
"Other-Play" for Zero-Shot Coordination. ICML (2020)
Dmitrii Beloborodov, Alexander E. Ulanov, Jakob N. Foerster, Shimon Whiteson, A. I. Lvovsky
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization. CoRR (2020)
Thomas D. Barrett, William R. Clements, Jakob N. Foerster, A. I. Lvovsky
Exploratory Combinatorial Optimization with Reinforcement Learning. AAAI (2020)
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling
The Hanabi challenge: A new frontier for AI research. Artif. Intell. 280 (2020)
Kalesha Bullard, Franziska Meier, Douwe Kiela, Joelle Pineau, Jakob N. Foerster
Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations. CoRR (2020)
Tabish Rashid, Mikayel Samvelyan, Christian Schröder de Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. CoRR (2020)
Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob N. Foerster
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. NeurIPS (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Jakob N. Foerster