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• affiliation:University of Oxford, UK

• Jakob Foerster

• 2025
• 
  [i124]
  J. Rosser,Jakob Nicolaus Foerster:
  AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds.CoRRabs/2502.00757 (2025)
• 
  [i123]
  Darius Muglich,Johannes Forkel,Elise van der Pol,Jakob N. Foerster:
  Expected Return Symmetries.CoRRabs/2502.01711 (2025)
• 
  [i122]
  Peer Nagy,Sascha Frey,Kang Li,Bidipta Sarkar,Svitlana Vyetrenko,Stefan Zohren,Ani Calinescu,Jakob N. Foerster:
  LOB-Bench: Benchmarking Generative AI for Finance - an Application to Limit Order Book Data.CoRRabs/2502.09172 (2025)
• 
  [i121]
  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,Vojtech Kovarík,Jan Kulveit,Joel Z. Leibo,Caspar Oesterheld,Christian Schröder de Witt,Nisarg Shah,Michael P. Wellman,Paolo Bova,Theodor Cimpeanu,Carson Ezell,Quentin Feuillade-Montixi,Matija Franklin,Esben Kran,Igor Krawczuk,Max Lamparth,Niklas Lauffer,Alexander Meinke,Sumeet Motwani,Anka Reuel,Vincent Conitzer,Michael Dennis,Iason Gabriel,Adam Gleave,Gillian K. Hadfield,Nika Haghtalab,Atoosa Kasirzadeh,Sébastien Krier,Kate Larson,Joel Lehman,David C. Parkes,Georgios Piliouras,Iyad Rahwan:
  Multi-Agent Risks from Advanced AI.CoRRabs/2502.14143 (2025)
• 
  [i120]
  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,Tatiana Shavrina,Jakob N. Foerster,Yoram Bachrach,William Yang Wang,Roberta Raileanu:
  MLGym: A New Framework and Benchmark for Advancing AI Research Agents.CoRRabs/2502.14499 (2025)
• 
  [i119]
  Tobias Gessler,Tin Dizdarevic,Ani Calinescu,Benjamin Ellis,Andrei Lupu,Jakob Nicolaus Foerster:
  OvercookedV2: Rethinking Overcooked for Zero-Shot Coordination.CoRRabs/2503.17821 (2025)
• 2024
• 
  [j7]
  Timon Willi,Johan Samir Obando-Ceron,Jakob Nicolaus Foerster,Gintare Karolina Dziugaite,Pablo Samuel Castro:
  Mixture of Experts in a Mixture of RL settings.RLJ3:1072-1105 (2024)
• 
  [j6]
  Matthew Thomas Jackson,Michael T. Matthews,Cong Lu,Benjamin Ellis,Shimon Whiteson,Jakob Nicolaus Foerster:
  Policy-Guided Diffusion.RLJ4:1855-1872 (2024)
• 
  [c90]
  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)2024:12702-12716
• 
  [c89]
  Kitty Fung,Qizhen Zhang,Chris Lu,Jia Wan,Timon Willi,Jakob N. Foerster:
  Analysing the Sample Complexity of Opponent Shaping.AAMAS2024:623-631
• 
  [c88]
  Akbir Khan,Timon Willi,Newton Kwan,Andrea Tacchetti,Chris Lu,Edward Grefenstette,Tim Rocktäschel,Jakob N. Foerster:
  Scaling Opponent Shaping to High Dimensional Games.AAMAS2024:1001-1010
• 
  [c87]
  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.AAMAS2024:1445-1453
• 
  [c86]
  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.AAMAS2024:2444-2446
• 
  [c85]
  Tim Franzmeyer,Edith Elkind,Philip Torr,Jakob Nicolaus Foerster,João F. Henriques:
  Select to Perfect: Imitating desired behavior from large multi-agent data.ICLR2024
• 
  [c84]
  Tim Franzmeyer,Stephen Marcus McAleer,João F. Henriques,Jakob Nicolaus Foerster,Philip Torr,Adel Bibi,Christian Schröder de Witt:
  Illusory Attacks: Information-theoretic detectability matters in adversarial attacks.ICLR2024
• 
  [c83]
  Matthew Thomas Jackson,Chris Lu,Louis Kirsch,Robert Tjarko Lange,Shimon Whiteson,Jakob Nicolaus Foerster:
  Discovering Temporally-Aware Reinforcement Learning Algorithms.ICLR2024
• 
  [c82]
  Yat Long Lo,Biswa Sengupta,Jakob Nicolaus Foerster,Michael Noukhovitch:
  Learning Multi-Agent Communication with Contrastive Learning.ICLR2024
• 
  [c81]
  Andrei Lupu,Chris Lu,Jarek Liesen,Robert Tjarko Lange,Jakob Nicolaus Foerster:
  Behaviour Distillation.ICLR2024
• 
  [c80]
  Michael Beukman,Samuel Coward,Michael T. Matthews,Mattie Fellows,Minqi Jiang,Michael D. Dennis,Jakob Nicolaus Foerster:
  Refining Minimax Regret for Unsupervised Environment Design.ICML2024
• 
  [c79]
  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 Thomas Jackson,Paul Röttger,Philip H. S. Torr,Trevor Darrell,Yong Suk Lee,Jakob N. Foerster:
  Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI.ICML2024
• 
  [c78]
  Andrew Jesson,Chris Lu,Gunshi Gupta,Nicolas Beltran-Velez,Angelos Filos,Jakob Nicolaus Foerster,Yarin Gal:
  ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages.ICML2024
• 
  [c77]
  Michael T. Matthews,Michael Beukman,Benjamin Ellis,Mikayel Samvelyan,Matthew Thomas Jackson,Samuel Coward,Jakob Nicolaus Foerster:
  Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning.ICML2024
• 
  [c76]
  Johan Samir Obando-Ceron,Ghada Sokar,Timon Willi,Clare Lyle,Jesse Farebrother,Jakob Nicolaus Foerster,Gintare Karolina Dziugaite,Doina Precup,Pablo Samuel Castro:
  Mixtures of Experts Unlock Parameter Scaling for Deep RL.ICML2024
• 
  [c75]
  Silvia Sapora,Gokul Swamy,Chris Lu,Yee Whye Teh,Jakob Nicolaus Foerster:
  EvIL: Evolution Strategies for Generalisable Imitation Learning.ICML2024
• 
  [c74]
  Ziyang Zhang,Qizhen Zhang,Jakob Nicolaus Foerster:
  PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition.ICML2024
• 
  [c73]
  Chris Lu,Samuel Holt,Claudio Fanconi,Alex J. Chan,Jakob N. Foerster,Mihaela van der Schaar,Robert T. Lange:
  Discovering Preference Optimization Algorithms with and for Large Language Models.NeurIPS2024
• 
  [c72]
  Jonathan Cook,Chris Lu,Edward Hughes,Joel Z. Leibo,Jakob N. Foerster:
  Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning.NeurIPS2024
• 
  [c71]
  Benjamin Ellis,Matthew Thomas Jackson,Andrei Lupu,Alexander David Goldie,Mattie Fellows,Shimon Whiteson,Jakob N. Foerster:
  Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps.NeurIPS2024
• 
  [c70]
  Alexander David Goldie,Chris Lu,Matthew Thomas Jackson,Shimon Whiteson,Jakob N. Foerster:
  Can Learned Optimization Make Reinforcement Learning Less Difficult?NeurIPS2024
• 
  [c69]
  Steven D. Morad,Chris Lu,Ryan Kortvelesy,Stephan Liwicki,Jakob N. Foerster,Amanda Prorok:
  Recurrent Reinforcement Learning with Memoroids.NeurIPS2024
• 
  [c68]
  Alexander Rutherford,Michael Beukman,Timon Willi,Bruno Lacerda,Nick Hawes,Jakob N. Foerster:
  No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery.NeurIPS2024
• 
  [c67]
  Alexander Rutherford,Benjamin Ellis,Matteo Gallici,Jonathan Cook,Andrei Lupu,Garðar Ingvarsson,Timon Willi,Ravi Hammond,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.NeurIPS2024
• 
  [c66]
  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,Jiawei Huang,Benjamin Swain,Haoyuan Qin,Mian Deng,Ziwei Deng,Utku Erdoganaras,Yue Zhao,Marko Tesic,Natasha Jaques,Jakob N. Foerster,Vincent Conitzer,José Hernández-Orallo,Dylan Hadfield-Menell,Joel Z. Leibo:
  Melting Pot Contest: Charting the Future of Generalized Cooperative Intelligence.NeurIPS2024
• 
  [c65]
  Qizhen (Irene) Zhang,Nikolas Gritsch,Dwaraknath Gnaneshwar,Simon Guo,David Cairuz,Bharat Venkitesh,Jakob N. Foerster,Phil Blunsom,Sebastian Ruder,Ahmet Üstün,Acyr Locatelli:
  BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts.NeurIPS2024
• 
  [c64]
  Uljad Berdica,Matthew Thomas Jackson,Niccolò Enrico Veronese,Jakob N. Foerster,Perla Maiolino:
  Reinforcement Learning Controllers for Soft Robots Using Learned Environments.RoboSoft2024:933-939
• 
  [c63]
  Samuel Sokota,Dylan Sam,Christian Schröder de Witt,Spencer Compton,Jakob N. Foerster,J. Zico Kolter:
  Computing Low-Entropy Couplings for Large-Support Distributions.UAI2024:3279-3298
• 
  [i118]
  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.CoRRabs/2402.01088 (2024)
• 
  [i117]
  Kitty Fung,Qizhen Zhang,Chris Lu,Jia Wan,Timon Willi,Jakob N. Foerster:
  Analysing the Sample Complexity of Opponent Shaping.CoRRabs/2402.05782 (2024)
• 
  [i116]
  Matthew Thomas Jackson,Chris Lu,Louis Kirsch,Robert T. Lange,Shimon Whiteson,Jakob Nicolaus Foerster:
  Discovering Temporally-Aware Reinforcement Learning Algorithms.CoRRabs/2402.05828 (2024)
• 
  [i115]
  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.CoRRabs/2402.08609 (2024)
• 
  [i114]
  Steven D. Morad,Chris Lu,Ryan Kortvelesy,Stephan Liwicki,Jakob N. Foerster,Amanda Prorok:
  Revisiting Recurrent Reinforcement Learning with Memory Monoids.CoRRabs/2402.09900 (2024)
• 
  [i113]
  Michael Beukman,Samuel Coward,Michael T. Matthews,Mattie Fellows,Minqi Jiang,Michael Dennis,Jakob N. Foerster:
  Refining Minimax Regret for Unsupervised Environment Design.CoRRabs/2402.12284 (2024)
• 
  [i112]
  Michael T. 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.CoRRabs/2402.16801 (2024)
• 
  [i111]
  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.CoRRabs/2402.16822 (2024)
• 
  [i110]
  Samuel Coward,Michael Beukman,Jakob N. Foerster:
  JaxUED: A simple and useable UED library in Jax.CoRRabs/2403.13091 (2024)
• 
  [i109]
  Matthew Thomas Jackson,Michael T. Matthews,Cong Lu,Benjamin Ellis,Shimon Whiteson,Jakob N. Foerster:
  Policy-Guided Diffusion.CoRRabs/2404.06356 (2024)
• 
  [i108]
  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.CoRRabs/2404.07099 (2024)
• 
  [i107]
  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.CoRRabs/2404.09932 (2024)
• 
  [i106]
  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 Thomas 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.CoRRabs/2404.17047 (2024)
• 
  [i105]
  Tim Franzmeyer,Edith Elkind,Philip Torr,Jakob N. Foerster,João F. Henriques:
  Select to Perfect: Imitating desired behavior from large multi-agent data.CoRRabs/2405.03735 (2024)
• 
  [i104]
  Ziyang Zhang,Qizhen Zhang,Jakob N. Foerster:
  PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition.CoRRabs/2405.07932 (2024)
• 
  [i103]
  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 Thomas Jackson,Philip H. S. Torr,Trevor Darrell,Yong Suk Lee,Jakob N. Foerster:
  Risks and Opportunities of Open-Source Generative AI.CoRRabs/2405.08597 (2024)
• 
  [i102]
  Samuel Sokota,Dylan Sam,Christian Schröder de Witt,Spencer Compton,Jakob N. Foerster,J. Zico Kolter:
  Computing Low-Entropy Couplings for Large-Support Distributions.CoRRabs/2405.19540 (2024)
• 
  [i101]
  Jonathan Cook,Chris Lu,Edward Hughes,Joel Z. Leibo,Jakob N. Foerster:
  Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning.CoRRabs/2406.00392 (2024)
• 
  [i100]
  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.CoRRabs/2406.03428 (2024)
• 
  [i99]
  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.CoRRabs/2406.08414 (2024)
• 
  [i98]
  Silvia Sapora,Gokul Swamy,Chris Lu,Yee Whye Teh,Jakob Nicolaus Foerster:
  EvIL: Evolution Strategies for Generalisable Imitation Learning.CoRRabs/2406.11905 (2024)
• 
  [i97]
  Jarek Liesen,Chris Lu,Andrei Lupu,Jakob N. Foerster,Henning Sprekeler,Robert T. Lange:
  Discovering Minimal Reinforcement Learning Environments.CoRRabs/2406.12589 (2024)
• 
  [i96]
  Andrei Lupu,Chris Lu,Jarek Liesen,Robert Tjarko Lange,Jakob N. Foerster:
  Behaviour Distillation.CoRRabs/2406.15042 (2024)
• 
  [i95]
  Timon Willi,Johan S. Obando-Ceron,Jakob N. Foerster,Karolina Dziugaite,Pablo Samuel Castro:
  Mixture of Experts in a Mixture of RL settings.CoRRabs/2406.18420 (2024)
• 
  [i94]
  Matteo Gallici,Mattie Fellows,Benjamin Ellis,Bartomeu Pou,Ivan Masmitja,Jakob Nicolaus Foerster,Mario Martin:
  Simplifying Deep Temporal Difference Learning.CoRRabs/2407.04811 (2024)
• 
  [i93]
  Alexander David Goldie,Chris Lu,Matthew Thomas Jackson,Shimon Whiteson,Jakob Nicolaus Foerster:
  Can Learned Optimization Make Reinforcement Learning Less Difficult?CoRRabs/2407.07082 (2024)
• 
  [i92]
  Chris Lu,Cong Lu,Robert Tjarko Lange,Jakob N. Foerster,Jeff Clune,David Ha:
  The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.CoRRabs/2408.06292 (2024)
• 
  [i91]
  Qizhen Zhang,Nikolas Gritsch,Dwaraknath Gnaneshwar,Simon Guo,David Cairuz,Bharat Venkitesh,Jakob N. Foerster,Phil Blunsom,Sebastian Ruder,Ahmet Üstün,Acyr Locatelli:
  BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts.CoRRabs/2408.08274 (2024)
• 
  [i90]
  Alexander Rutherford,Michael Beukman,Timon Willi,Bruno Lacerda,Nick Hawes,Jakob N. Foerster:
  No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery.CoRRabs/2408.15099 (2024)
• 
  [i89]
  Chris Lu,Michael Beukman,Michael T. Matthews,Jakob N. Foerster:
  JaxLife: An Open-Ended Agentic Simulator.CoRRabs/2409.00853 (2024)
• 
  [i88]
  Sebastian Towers,Aleksandra Kalisz,Philippe A. Robert,Alicia Higueruelo,Francesca V. Vianello,Ming-Han Chloe Tsai,Harrison Steel,Jakob N. Foerster:
  Opponent Shaping for Antibody Development.CoRRabs/2409.10588 (2024)
• 
  [i87]
  Jonathan Cook,Tim Rocktäschel,Jakob N. Foerster,Dennis Aumiller,Alex Wang:
  TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation.CoRRabs/2410.03608 (2024)
• 
  [i86]
  Uljad Berdica,Matthew Thomas Jackson,Niccolò Enrico Veronese,Jakob N. Foerster,Perla Maiolino:
  Reinforcement Learning Controllers for Soft Robots using Learned Environments.CoRRabs/2410.18519 (2024)
• 
  [i85]
  Lize Alberts,Benjamin Ellis,Andrei Lupu,Jakob N. Foerster:
  CURATe: Benchmarking Personalised Alignment of Conversational AI Assistants.CoRRabs/2410.21159 (2024)
• 
  [i84]
  Michael T. Matthews,Michael Beukman,Chris Lu,Jakob N. Foerster:
  Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks.CoRRabs/2410.23208 (2024)
• 
  [i83]
  Charlie B. Tan,Edan Toledo,Benjamin Ellis,Jakob N. Foerster,Ferenc Huszár:
  Beyond the Boundaries of Proximal Policy Optimization.CoRRabs/2411.00666 (2024)
• 
  [i82]
  Usman Anwar,Ashish Pandian,Jia Wan,David Krueger,Jakob N. Foerster:
  Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination Games.CoRRabs/2411.04976 (2024)
• 
  [i81]
  Carlo Alfano,Silvia Sapora,Jakob Nicolaus Foerster,Patrick Rebeschini,Yee Whye Teh:
  Learning Loss Landscapes in Preference Optimization.CoRRabs/2411.06568 (2024)
• 
  [i80]
  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,Tim Rocktäschel:
  BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games.CoRRabs/2411.13543 (2024)
• 
  [i79]
  Branton DeMoss,Silvia Sapora,Jakob N. Foerster,Nick Hawes,Ingmar Posner:
  The Complexity Dynamics of Grokking.CoRRabs/2412.09810 (2024)
• 
  [i78]
  Benjamin Ellis,Matthew Thomas Jackson,Andrei Lupu,Alexander David Goldie,Mattie Fellows,Shimon Whiteson,Jakob N. Foerster:
  Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps.CoRRabs/2412.17113 (2024)
• 2023
• 
  [j5]
  Isaac Liao,Rumen Dangovski,Jakob Nicolaus Foerster,Marin Soljacic:
  Learning to Optimize Quasi-Newton Methods.Trans. Mach. Learn. Res.2023 (2023)
• 
  [c62]
  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.ICAIF2023:91-99
• 
  [c61]
  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.ICAIF2023:583-591
• 
  [c60]
  Brandon Cui,Andrei Lupu,Samuel Sokota,Hengyuan Hu,David J. Wu,Jakob Nicolaus Foerster:
  Adversarial Diversity in Hanabi.ICLR2023
• 
  [c59]
  Yat Long Lo,Christian Schröder de Witt,Samuel Sokota,Jakob Nicolaus Foerster,Shimon Whiteson:
  Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning.ICLR2023
• 
  [c58]
  Mikayel Samvelyan,Akbir Khan,Michael Dennis,Minqi Jiang,Jack Parker-Holder,Jakob Nicolaus Foerster,Roberta Raileanu,Tim Rocktäschel:
  MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning.ICLR2023
• 
  [c57]
  Christian Schröder de Witt,Samuel Sokota,J. Zico Kolter,Jakob Nicolaus Foerster,Martin Strohmeier:
  Perfectly Secure Steganography Using Minimum Entropy Coupling.ICLR2023
• 
  [c56]
  Chris Lu,Timon Willi,Alistair Letcher,Jakob Nicolaus Foerster:
  Adversarial Cheap Talk.ICML2023:22917-22941
• 
  [c55]
  Mingwei Ma,Jizhou Liu,Samuel Sokota,Max Kleiman-Weiner,Jakob Nicolaus Foerster:
  Learning Intuitive Policies Using Action Features.ICML2023:23358-23372
• 
  [c54]
  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.NeurIPS2023
• 
  [c53]
  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.NeurIPS2023
• 
  [c52]
  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.NeurIPS2023
• 
  [c51]
  Caspar Oesterheld,Johannes Treutlein,Roger B. Grosse,Vincent Conitzer,Jakob N. Foerster:
  Similarity-based cooperative equilibrium.NeurIPS2023
• 
  [i77]
  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.CoRRabs/2303.03376 (2023)
• 
  [i76]
  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.CoRRabs/2303.03982 (2023)
• 
  [i75]
  Chris Lu,Sebastian Towers,Jakob N. Foerster:
  Arbitrary Order Meta-Learning with Simple Population-Based Evolution.CoRRabs/2303.09478 (2023)
• 
  [i74]
  Yat Long Lo,Christian Schröder de Witt,Samuel Sokota,Jakob Nicolaus Foerster,Shimon Whiteson:
  Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning.CoRRabs/2303.10733 (2023)
• 
  [i73]
  Andrew Jesson,Chris Lu,Gunshi Gupta,Angelos Filos,Jakob Nicolaus Foerster,Yarin Gal:
  ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages.CoRRabs/2306.01460 (2023)
• 
  [i72]
  Yat Long Lo,Biswa Sengupta,Jakob N. Foerster,Michael Noukhovitch:
  Learning to Communicate using Contrastive Learning.CoRRabs/2307.01403 (2023)
• 
  [i71]
  Elena Gal,Shaun Singh,Aldo Pacchiano,Ben Walker,Terry J. Lyons,Jakob N. Foerster:
  Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem.CoRRabs/2308.08051 (2023)
• 
  [i70]
  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.CoRRabs/2308.13289 (2023)
• 
  [i69]
  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.CoRRabs/2309.00638 (2023)
• 
  [i68]
  Matthew Thomas Jackson,Minqi Jiang,Jack Parker-Holder,Risto Vuorio,Chris Lu,Gregory Farquhar,Shimon Whiteson,Jakob Nicolaus Foerster:
  Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design.CoRRabs/2310.02782 (2023)
• 
  [i67]
  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 Tjarko Lange,Shimon Whiteson,Bruno Lacerda,Nick Hawes,Tim Rocktäschel,Chris Lu,Jakob Nicolaus Foerster:
  JaxMARL: Multi-Agent RL Environments in JAX.CoRRabs/2311.10090 (2023)
• 
  [i66]
  Akbir Khan,Timon Willi,Newton Kwan,Andrea Tacchetti,Chris Lu,Edward Grefenstette,Tim Rocktäschel,Jakob N. Foerster:
  Scaling Opponent Shaping to High Dimensional Games.CoRRabs/2312.12568 (2023)
• 2022
• 
  [c50]
  Jonathan Lorraine,Paul Vicol,Jack Parker-Holder,Tal Kachman,Luke Metz,Jakob N. Foerster:
  Lyapunov Exponents for Diversity in Differentiable Games.AAMAS2022:842-852
• 
  [c49]
  Qizhen Zhang,Christopher Lu,Animesh Garg,Jakob N. Foerster:
  Centralized Model and Exploration Policy for Multi-Agent RL.AAMAS2022:1500-1508
• 
  [c48]
  Samuel Sokota,Hengyuan Hu,David J. Wu,J. Zico Kolter,Jakob Nicolaus Foerster,Noam Brown:
  A Fine-Tuning Approach to Belief State Modeling.ICLR2022
• 
  [c47]
  Jakub Grudzien Kuba,Christian A. Schröder de Witt,Jakob N. Foerster:
  Mirror Learning: A Unifying Framework of Policy Optimisation.ICML2022:7825-7844
• 
  [c46]
  Christopher Lu,Timon Willi,Christian A. Schröder de Witt,Jakob N. Foerster:
  Model-Free Opponent Shaping.ICML2022:14398-14411
• 
  [c45]
  Darius Muglich,Luisa M. Zintgraf,Christian A. Schröder de Witt,Shimon Whiteson,Jakob N. Foerster:
  Generalized Beliefs for Cooperative AI.ICML2022:16062-16082
• 
  [c44]
  Jack Parker-Holder,Minqi Jiang,Michael Dennis,Mikayel Samvelyan,Jakob N. Foerster,Edward Grefenstette,Tim Rocktäschel:
  Evolving Curricula with Regret-Based Environment Design.ICML2022:17473-17498
• 
  [c43]
  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.ICML2022:20314-20328
• 
  [c42]
  Timon Willi,Alistair Letcher,Johannes Treutlein,Jakob N. Foerster:
  COLA: Consistent Learning with Opponent-Learning Awareness.ICML2022:23804-23831
• 
  [c41]
  Chris Lu,Jakub Grudzien Kuba,Alistair Letcher,Luke Metz,Christian Schröder de Witt,Jakob N. Foerster:
  Discovered Policy Optimisation.NeurIPS2022
• 
  [c40]
  Brandon Cui,Hengyuan Hu,Andrei Lupu,Samuel Sokota,Jakob N. Foerster:
  Off-Team Learning.NeurIPS2022
• 
  [c39]
  Hengyuan Hu,Samuel Sokota,David J. Wu,Anton Bakhtin,Andrei Lupu,Brandon Cui,Jakob N. Foerster:
  Self-Explaining Deviations for Coordination.NeurIPS2022
• 
  [c38]
  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.NeurIPS2022
• 
  [c37]
  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.NeurIPS2022
• 
  [c36]
  Darius Muglich,Christian Schröder de Witt,Elise van der Pol,Shimon Whiteson,Jakob N. Foerster:
  Equivariant Networks for Zero-Shot Coordination.NeurIPS2022
• 
  [c35]
  Stephen Zhao,Chris Lu,Roger B. Grosse,Jakob N. Foerster:
  Proximal Learning With Opponent-Learning Awareness.NeurIPS2022
• 
  [i65]
  Jakub Grudzien Kuba,Christian Schröder de Witt,Jakob N. Foerster:
  Mirror Learning: A Unifying Framework of Policy Optimisation.CoRRabs/2201.02373 (2022)
• 
  [i64]
  Mingwei Ma,Jizhou Liu,Samuel Sokota,Max Kleiman-Weiner,Jakob N. Foerster:
  Learning to Coordinate with Humans using Action Features.CoRRabs/2201.12658 (2022)
• 
  [i63]
  Jack Parker-Holder,Minqi Jiang,Michael Dennis,Mikayel Samvelyan,Jakob N. Foerster,Edward Grefenstette,Tim Rocktäschel:
  Evolving Curricula with Regret-Based Environment Design.CoRRabs/2203.01302 (2022)
• 
  [i62]
  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.CoRRabs/2203.03535 (2022)
• 
  [i61]
  Timon Willi,Johannes Treutlein,Alistair Letcher,Jakob N. Foerster:
  COLA: Consistent Learning with Opponent-Learning Awareness.CoRRabs/2203.04098 (2022)
• 
  [i60]
  Christopher Lu,Timon Willi,Christian Schröder de Witt,Jakob N. Foerster:
  Model-Free Opponent Shaping.CoRRabs/2205.01447 (2022)
• 
  [i59]
  Darius Muglich,Luisa M. Zintgraf,Christian Schröder de Witt,Shimon Whiteson,Jakob N. Foerster:
  Generalized Beliefs for Cooperative AI.CoRRabs/2206.12765 (2022)
• 
  [i58]
  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.CoRRabs/2207.05219 (2022)
• 
  [i57]
  Brandon Cui,Hengyuan Hu,Luis Pineda,Jakob N. Foerster:
  K-level Reasoning for Zero-Shot Coordination in Hanabi.CoRRabs/2207.07166 (2022)
• 
  [i56]
  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.CoRRabs/2207.10170 (2022)
• 
  [i55]
  Hengyuan Hu,Samuel Sokota,David J. Wu,Anton Bakhtin,Andrei Lupu,Brandon Cui,Jakob N. Foerster:
  Self-Explaining Deviations for Coordination.CoRRabs/2207.12322 (2022)
• 
  [i54]
  Risto Vuorio,Jacob Beck,Shimon Whiteson,Jakob N. Foerster,Gregory Farquhar:
  An Investigation of the Bias-Variance Tradeoff in Meta-Gradients.CoRRabs/2209.11303 (2022)
• 
  [i53]
  Hengyuan Hu,David J. Wu,Adam Lerer,Jakob N. Foerster,Noam Brown:
  Human-AI Coordination via Human-Regularized Search and Learning.CoRRabs/2210.05125 (2022)
• 
  [i52]
  Chris Lu,Jakub Grudzien Kuba,Alistair Letcher,Luke Metz,Christian Schröder de Witt,Jakob N. Foerster:
  Discovered Policy Optimisation.CoRRabs/2210.05639 (2022)
• 
  [i51]
  Isaac Liao,Rumen R. Dangovski,Jakob N. Foerster,Marin Soljacic:
  Learning to Optimize Quasi-Newton Methods.CoRRabs/2210.06171 (2022)
• 
  [i50]
  Stephen Zhao,Chris Lu,Roger Baker Grosse,Jakob Nicolaus Foerster:
  Proximal Learning With Opponent-Learning Awareness.CoRRabs/2210.10125 (2022)
• 
  [i49]
  Darius Muglich,Christian Schröder de Witt,Elise van der Pol,Shimon Whiteson,Jakob N. Foerster:
  Equivariant Networks for Zero-Shot Coordination.CoRRabs/2210.12124 (2022)
• 
  [i48]
  Christian Schröder de Witt,Samuel Sokota,J. Zico Kolter,Jakob N. Foerster,Martin Strohmeier:
  Perfectly Secure Steganography Using Minimum Entropy Coupling.CoRRabs/2210.14889 (2022)
• 
  [i47]
  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.CoRRabs/2210.16175 (2022)
• 
  [i46]
  Chris Lu,Timon Willi,Alistair Letcher,Jakob N. Foerster:
  Adversarial Cheap Talk.CoRRabs/2211.11030 (2022)
• 
  [i45]
  Caspar Oesterheld,Johannes Treutlein,Roger B. Grosse,Vincent Conitzer,Jakob N. Foerster:
  Similarity-based Cooperation.CoRRabs/2211.14468 (2022)
• 
  [i44]
  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.CoRRabs/2212.07489 (2022)
• 2021
• 
  [j4]
  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):25009 (2021)
• 
  [c34]
  Andrei Lupu,Hengyuan Hu,Jakob N. Foerster:
  Trajectory Diversity for Zero-Shot Coordination.AAMAS2021:1593-1595
• 
  [c33]
  Hengyuan Hu,Adam Lerer,Brandon Cui,Luis Pineda,Noam Brown,Jakob N. Foerster:
  Off-Belief Learning.ICML2021:4369-4379
• 
  [c32]
  Andrei Lupu,Brandon Cui,Hengyuan Hu,Jakob N. Foerster:
  Trajectory Diversity for Zero-Shot Coordination.ICML2021:7204-7213
• 
  [c31]
  Johannes Treutlein,Michael Dennis,Caspar Oesterheld,Jakob N. Foerster:
  A New Formalism, Method and Open Issues for Zero-Shot Coordination.ICML2021:10413-10423
• 
  [c30]
  Minqi Jiang,Michael Dennis,Jack Parker-Holder,Jakob N. Foerster,Edward Grefenstette,Tim Rocktäschel:
  Replay-Guided Adversarial Environment Design.NeurIPS2021:1884-1897
• 
  [c29]
  Aldo Pacchiano,Shaun Singh,Edward Chou,Alexander C. Berg,Jakob N. Foerster:
  Neural Pseudo-Label Optimism for the Bank Loan Problem.NeurIPS2021:6580-6593
• 
  [c28]
  Brandon Cui,Hengyuan Hu,Luis Pineda,Jakob N. Foerster:
  K-level Reasoning for Zero-Shot Coordination in Hanabi.NeurIPS2021:8215-8228
• 
  [i43]
  Hengyuan Hu,Adam Lerer,Brandon Cui,Luis Pineda,David J. Wu,Noam Brown,Jakob N. Foerster:
  Off-Belief Learning.CoRRabs/2103.04000 (2021)
• 
  [i42]
  Kalesha Bullard,Douwe Kiela,Joelle Pineau,Jakob N. Foerster:
  Quasi-Equivalence Discovery for Zero-Shot Emergent Communication.CoRRabs/2103.08067 (2021)
• 
  [i41]
  Johannes Treutlein,Michael Dennis,Caspar Oesterheld,Jakob N. Foerster:
  A New Formalism, Method and Open Issues for Zero-Shot Coordination.CoRRabs/2106.06613 (2021)
• 
  [i40]
  Hengyuan Hu,Adam Lerer,Noam Brown,Jakob N. Foerster:
  Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings.CoRRabs/2106.09086 (2021)
• 
  [i39]
  Qizhen Zhang,Christopher Lu,Animesh Garg,Jakob N. Foerster:
  Centralized Model and Exploration Policy for Multi-Agent RL.CoRRabs/2107.06434 (2021)
• 
  [i38]
  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.CoRRabs/2107.08295 (2021)
• 
  [i37]
  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.CoRRabs/2107.12460 (2021)
• 
  [i36]
  Minqi Jiang,Michael Dennis,Jack Parker-Holder,Jakob N. Foerster,Edward Grefenstette,Tim Rocktäschel:
  Replay-Guided Adversarial Environment Design.CoRRabs/2110.02439 (2021)
• 
  [i35]
  Aldo Pacchiano,Shaun Singh,Edward Chou,Alexander C. Berg,Jakob N. Foerster:
  Neural Pseudo-Label Optimism for the Bank Loan Problem.CoRRabs/2112.02185 (2021)
• 
  [i34]
  Jonathan Lorraine,Paul Vicol,Jack Parker-Holder,Tal Kachman,Luke Metz,Jakob N. Foerster:
  Lyapunov Exponents for Diversity in Differentiable Games.CoRRabs/2112.14570 (2021)
• 2020
• 
  [j3]
  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.280:103216 (2020)
• 
  [j2]
  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.J. Mach. Learn. Res.21:178:1-178:51 (2020)
• 
  [c27]
  Thomas D. Barrett,William R. Clements,Jakob N. Foerster,A. I. Lvovsky:
  Exploratory Combinatorial Optimization with Reinforcement Learning.AAAI2020:3243-3250
• 
  [c26]
  Adam Lerer,Hengyuan Hu,Jakob N. Foerster,Noam Brown:
  Improving Policies via Search in Cooperative Partially Observable Games.AAAI2020:7187-7194
• 
  [c25]
  Cinjon Resnick,Abhinav Gupta,Jakob N. Foerster,Andrew M. Dai,Kyunghyun Cho:
  Capacity, Bandwidth, and Compositionality in Emergent Language Learning.AAMAS2020:1125-1133
• 
  [c24]
  Hengyuan Hu,Jakob N. Foerster:
  Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning.ICLR2020
• 
  [c23]
  Ryan Lowe,Abhinav Gupta,Jakob N. Foerster,Douwe Kiela,Joelle Pineau:
  On the interaction between supervision and self-play in emergent communication.ICLR2020
• 
  [c22]
  Hengyuan Hu,Adam Lerer,Alex Peysakhovich,Jakob N. Foerster:
  "Other-Play" for Zero-Shot Coordination.ICML2020:4399-4410
• 
  [c21]
  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.NeurIPS2020
• 
  [c20]
  Abhinav Gupta,Cinjon Resnick,Jakob N. Foerster,Andrew M. Dai,Kyunghyun Cho:
  Compositionality and Capacity in Emergent Languages.RepL4NLP@ACL2020:34-38
• 
  [i33]
  Ryan Lowe,Abhinav Gupta,Jakob N. Foerster,Douwe Kiela,Joelle Pineau:
  On the interaction between supervision and self-play in emergent communication.CoRRabs/2002.01093 (2020)
• 
  [i32]
  Dmitrii Beloborodov,Alexander E. Ulanov,Jakob N. Foerster,Shimon Whiteson,A. I. Lvovsky:
  Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization.CoRRabs/2002.04676 (2020)
• 
  [i31]
  Hengyuan Hu,Adam Lerer,Alex Peysakhovich,Jakob N. Foerster:
  "Other-Play" for Zero-Shot Coordination.CoRRabs/2003.02979 (2020)
• 
  [i30]
  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.CoRRabs/2003.08839 (2020)
• 
  [i29]
  Oana-Maria Camburu,Eleonora Giunchiglia,Jakob N. Foerster,Thomas Lukasiewicz,Phil Blunsom:
  The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets.CoRRabs/2009.11023 (2020)
• 
  [i28]
  Kalesha Bullard,Franziska Meier,Douwe Kiela,Joelle Pineau,Jakob N. Foerster:
  Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations.CoRRabs/2010.15896 (2020)
• 
  [i27]
  Jack Parker-Holder,Luke Metz,Cinjon Resnick,Hengyuan Hu,Adam Lerer,Alistair Letcher,Alex Peysakhovich,Aldo Pacchiano,Jakob N. Foerster:
  Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian.CoRRabs/2011.06505 (2020)

• 2019
• 
  [j1]
  Alistair Letcher,David Balduzzi,Sébastien Racanière,James Martens,Jakob N. Foerster,Karl Tuyls,Thore Graepel:
  Differentiable Game Mechanics.J. Mach. Learn. Res.20:84:1-84:40 (2019)
• 
  [c19]
  Ryan Lowe,Jakob N. Foerster,Y-Lan Boureau,Joelle Pineau,Yann N. Dauphin:
  On the Pitfalls of Measuring Emergent Communication.AAMAS2019:693-701
• 
  [c18]
  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:
  The StarCraft Multi-Agent Challenge.AAMAS2019:2186-2188
• 
  [c17]
  Abhinav Gupta,Ryan Lowe,Jakob N. Foerster,Douwe Kiela,Joelle Pineau:
  Seeded self-play for language learning.LANTERN@EMNLP-IJCNLP2019:62-66
• 
  [c16]
  Alistair Letcher,Jakob N. Foerster,David Balduzzi,Tim Rocktäschel,Shimon Whiteson:
  Stable Opponent Shaping in Differentiable Games.ICLR (Poster)2019
• 
  [c15]
  Jakob N. Foerster,H. Francis Song,Edward Hughes,Neil Burch,Iain Dunning,Shimon Whiteson,Matthew M. Botvinick,Michael Bowling:
  Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning.ICML2019:1942-1951
• 
  [c14]
  Jingkai Mao,Jakob N. Foerster,Tim Rocktäschel,Maruan Al-Shedivat,Gregory Farquhar,Shimon Whiteson:
  A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs.ICML2019:4343-4351
• 
  [c13]
  Jelena Luketina,Nantas Nardelli,Gregory Farquhar,Jakob N. Foerster,Jacob Andreas,Edward Grefenstette,Shimon Whiteson,Tim Rocktäschel:
  A Survey of Reinforcement Learning Informed by Natural Language.IJCAI2019:6309-6317
• 
  [c12]
  Gregory Farquhar,Shimon Whiteson,Jakob N. Foerster:
  Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning.NeurIPS2019:8149-8160
• 
  [c11]
  Christian Schröder de Witt,Jakob N. Foerster,Gregory Farquhar,Philip H. S. Torr,Wendelin Boehmer,Shimon Whiteson:
  Multi-Agent Common Knowledge Reinforcement Learning.NeurIPS2019:9924-9935
• 
  [i26]
  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.CoRRabs/1902.00506 (2019)
• 
  [i25]
  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:
  The StarCraft Multi-Agent Challenge.CoRRabs/1902.04043 (2019)
• 
  [i24]
  Ryan Lowe,Jakob N. Foerster,Y-Lan Boureau,Joelle Pineau,Yann N. Dauphin:
  On the Pitfalls of Measuring Emergent Communication.CoRRabs/1903.05168 (2019)
• 
  [i23]
  Alistair Letcher,David Balduzzi,Sébastien Racanière,James Martens,Jakob N. Foerster,Karl Tuyls,Thore Graepel:
  Differentiable Game Mechanics.CoRRabs/1905.04926 (2019)
• 
  [i22]
  Jelena Luketina,Nantas Nardelli,Gregory Farquhar,Jakob N. Foerster,Jacob Andreas,Edward Grefenstette,Shimon Whiteson,Tim Rocktäschel:
  A Survey of Reinforcement Learning Informed by Natural Language.CoRRabs/1906.03926 (2019)
• 
  [i21]
  Thomas D. Barrett,William R. Clements,Jakob N. Foerster,A. I. Lvovsky:
  Exploratory Combinatorial Optimization with Reinforcement Learning.CoRRabs/1909.04063 (2019)
• 
  [i20]
  Gregory Farquhar,Shimon Whiteson,Jakob N. Foerster:
  Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning.CoRRabs/1909.10549 (2019)
• 
  [i19]
  Oana-Maria Camburu,Eleonora Giunchiglia,Jakob N. Foerster,Thomas Lukasiewicz,Phil Blunsom:
  Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods.CoRRabs/1910.02065 (2019)
• 
  [i18]
  Reda Bahi Slaoui,William R. Clements,Jakob N. Foerster,Sébastien Toth:
  Robust Domain Randomization for Reinforcement Learning.CoRRabs/1910.10537 (2019)
• 
  [i17]
  Cinjon Resnick,Abhinav Gupta,Jakob N. Foerster,Andrew M. Dai,Kyunghyun Cho:
  Capacity, Bandwidth, and Compositionality in Emergent Language Learning.CoRRabs/1910.11424 (2019)
• 
  [i16]
  Hengyuan Hu,Jakob N. Foerster:
  Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning.CoRRabs/1912.02288 (2019)
• 
  [i15]
  Adam Lerer,Hengyuan Hu,Jakob N. Foerster,Noam Brown:
  Improving Policies via Search in Cooperative Partially Observable Games.CoRRabs/1912.02318 (2019)
• 2018
• 
  [b1]
  Jakob N. Foerster:
  Deep multi-agent reinforcement learning. University of Oxford, UK,2018
• 
  [c10]
  Jakob N. Foerster,Gregory Farquhar,Triantafyllos Afouras,Nantas Nardelli,Shimon Whiteson:
  Counterfactual Multi-Agent Policy Gradients.AAAI2018:2974-2982
• 
  [c9]
  Cinjon Resnick,Wes Eldridge,David Ha,Denny Britz,Jakob N. Foerster,Julian Togelius,Kyunghyun Cho,Joan Bruna:
  Pommerman: A Multi-Agent Playground.AIIDE Workshops2018
• 
  [c8]
  Jakob N. Foerster,Richard Y. Chen,Maruan Al-Shedivat,Shimon Whiteson,Pieter Abbeel,Igor Mordatch:
  Learning with Opponent-Learning Awareness.AAMAS2018:122-130
• 
  [c7]
  Jakob N. Foerster,Gregory Farquhar,Maruan Al-Shedivat,Tim Rocktäschel,Eric P. Xing,Shimon Whiteson:
  DiCE: The Infinitely Differentiable Monte-Carlo Estimator.ICLR (Workshop)2018
• 
  [c6]
  David Balduzzi,Sébastien Racanière,James Martens,Jakob N. Foerster,Karl Tuyls,Thore Graepel:
  The Mechanics of n-Player Differentiable Games.ICML2018:363-372
• 
  [c5]
  Jakob N. Foerster,Gregory Farquhar,Maruan Al-Shedivat,Tim Rocktäschel,Eric P. Xing,Shimon Whiteson:
  DiCE: The Infinitely Differentiable Monte Carlo Estimator.ICML2018:1524-1533
• 
  [c4]
  Tabish Rashid,Mikayel Samvelyan,Christian Schröder de Witt,Gregory Farquhar,Jakob N. Foerster,Shimon Whiteson:
  QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.ICML2018:4292-4301
• 
  [i14]
  Jakob N. Foerster,Gregory Farquhar,Maruan Al-Shedivat,Tim Rocktäschel,Eric P. Xing,Shimon Whiteson:
  DiCE: The Infinitely Differentiable Monte-Carlo Estimator.CoRRabs/1802.05098 (2018)
• 
  [i13]
  David Balduzzi,Sébastien Racanière,James Martens,Jakob N. Foerster,Karl Tuyls,Thore Graepel:
  The Mechanics of n-Player Differentiable Games.CoRRabs/1802.05642 (2018)
• 
  [i12]
  Tabish Rashid,Mikayel Samvelyan,Christian Schröder de Witt,Gregory Farquhar,Jakob N. Foerster,Shimon Whiteson:
  QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.CoRRabs/1803.11485 (2018)
• 
  [i11]
  Cinjon Resnick,Wes Eldridge,David Ha,Denny Britz,Jakob N. Foerster,Julian Togelius,Kyunghyun Cho,Joan Bruna:
  Pommerman: A Multi-Agent Playground.CoRRabs/1809.07124 (2018)
• 
  [i10]
  Jakob N. Foerster,Christian A. Schröder de Witt,Gregory Farquhar,Philip H. S. Torr,Wendelin Boehmer,Shimon Whiteson:
  Multi-Agent Common Knowledge Reinforcement Learning.CoRRabs/1810.11702 (2018)
• 
  [i9]
  Jakob N. Foerster,H. Francis Song,Edward Hughes,Neil Burch,Iain Dunning,Shimon Whiteson,Matthew M. Botvinick,Michael Bowling:
  Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning.CoRRabs/1811.01458 (2018)
• 
  [i8]
  Alistair Letcher,Jakob N. Foerster,David Balduzzi,Tim Rocktäschel,Shimon Whiteson:
  Stable Opponent Shaping in Differentiable Games.CoRRabs/1811.08469 (2018)
• 2017
• 
  [c3]
  Jakob N. Foerster,Justin Gilmer,Jascha Sohl-Dickstein,Jan Chorowski,David Sussillo:
  Input Switched Affine Networks: An RNN Architecture Designed for Interpretability.ICML2017:1136-1145
• 
  [c2]
  Jakob N. Foerster,Nantas Nardelli,Gregory Farquhar,Triantafyllos Afouras,Philip H. S. Torr,Pushmeet Kohli,Shimon Whiteson:
  Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning.ICML2017:1146-1155
• 
  [i7]
  Jakob N. Foerster,Nantas Nardelli,Gregory Farquhar,Philip H. S. Torr,Pushmeet Kohli,Shimon Whiteson:
  Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning.CoRRabs/1702.08887 (2017)
• 
  [i6]
  Jakob N. Foerster,Gregory Farquhar,Triantafyllos Afouras,Nantas Nardelli,Shimon Whiteson:
  Counterfactual Multi-Agent Policy Gradients.CoRRabs/1705.08926 (2017)
• 
  [i5]
  Christoph Aymanns,Jakob N. Foerster,Co-Pierre Georg:
  Fake News in Social Networks.CoRRabs/1708.06233 (2017)
• 
  [i4]
  Jakob N. Foerster,Richard Y. Chen,Maruan Al-Shedivat,Shimon Whiteson,Pieter Abbeel,Igor Mordatch:
  Learning with Opponent-Learning Awareness.CoRRabs/1709.04326 (2017)
• 2016
• 
  [c1]
  Jakob N. Foerster,Yannis M. Assael,Nando de Freitas,Shimon Whiteson:
  Learning to Communicate with Deep Multi-Agent Reinforcement Learning.NIPS2016:2137-2145
• 
  [i3]
  Jakob N. Foerster,Yannis M. Assael,Nando de Freitas,Shimon Whiteson:
  Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks.CoRRabs/1602.02672 (2016)
• 
  [i2]
  Jakob N. Foerster,Yannis M. Assael,Nando de Freitas,Shimon Whiteson:
  Learning to Communicate with Deep Multi-Agent Reinforcement Learning.CoRRabs/1605.06676 (2016)
• 
  [i1]
  Jakob N. Foerster,Justin Gilmer,Jan Chorowski,Jascha Sohl-Dickstein,David Sussillo:
  Intelligible Language Modeling with Input Switched Affine Networks.CoRRabs/1611.09434 (2016)