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ORCIDJakob N. Foerster
Jakob Nicolaus Foerster
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- affiliation: University of Oxford, UK
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2020 – today
- 2026
[i156]Willem Röpke, Samuel Coward, Andrei Lupu, Thomas Foster, Tim Rocktäschel, Jakob N. Foerster:
DéjàQ: Open-Ended Evolution of Diverse, Learnable and Verifiable Problems. CoRR abs/2601.01931 (2026)- [i155]Harry Mead, Bruno Lacerda, Jakob N. Foerster, Nick Hawes:
Improving Regret Approximation for Unsupervised Dynamic Environment Generation. CoRR abs/2601.14957 (2026) - 2025
- [c106]Qizhen Zhang, Prajjwal Bhargava, Chloe Bi, Chris X. Cai, Jakob Nicolaus Foerster, Jeremy Fu, Punit Singh Koura, Ruan Silva, Sheng Shen, Emily Dinan, Suchin Gururangan, Mike Lewis:
BTS: Harmonizing Specialized Experts into a Generalist LLM. EMNLP 2025: 6816-6834 - [c105]Valentin Mohl
, Sascha Frey, Reuben Leyland, Kang Li, George Nigmatulin, Mihai Cucuringu, Stefan Zohren, Jakob N. Foerster, Anisoara Calinescu:
JaxMARL-HFT: GPU-Accelerated Large-Scale Multi-Agent Reinforcement Learning for High-Frequency Trading. ICAIF 2025: 18-26 - [c104]Kang Li, Bidipta Sarkar, Zheng Xiong, Sascha Frey, Zilin Wang, Frensi Zejnullahu, Alfred Backhouse, Stefan Zohren, Anisoara Calinescu, Mihai Cucuringu, Jakob N. Foerster:
Discrete Flow Matching is a Surprisingly Effective Post-training Method to Address Compound Error in Autoregressive Models. ICAIF 2025: 211-219 - [c103]Matteo Gallici, Mattie Fellows, Benjamin Ellis, Bartomeu Pou, Ivan Masmitja, Jakob Nicolaus Foerster, Mario Martin:
Simplifying Deep Temporal Difference Learning. ICLR 2025 - [c102]Tobias Gessler, Tin Dizdarevic, Ani Calinescu, Benjamin Ellis, Andrei Lupu, Jakob Nicolaus Foerster:
OvercookedV2: Rethinking Overcooked for Zero-Shot Coordination. ICLR 2025 - [c101]Michael T. Matthews, Michael Beukman, Chris Lu, Jakob Nicolaus Foerster:
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks. ICLR 2025 - [c100]Darius Muglich, Johannes Forkel, Elise van der Pol, Jakob Nicolaus Foerster:
Expected Return Symmetries. ICLR 2025 - [c99]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. ICLR 2025 - [c98]Tin Dizdarevic, Ravi Hammond, Tobias Gessler, Anisoara Calinescu, Jonathan Cook, Matteo Gallici, Andrei Lupu, Jakob Nicolaus Foerster:
Ad-Hoc Human-AI Coordination Challenge. ICML 2025 - [c97]Peer Nagy, Sascha Yves Frey, Kang Li, Bidipta Sarkar, Svitlana Vyetrenko, Stefan Zohren, Ani Calinescu, Jakob Nicolaus Foerster:
LOB-Bench: Benchmarking Generative AI for Finance - an Application to Limit Order Book Data. ICML 2025 - [c96]Sebastian Rene Towers, Aleksandra Kalisz, Philippe A. Robert, Alicia Higueruelo, Francesca V. Vianello, Ming-Han Chloe Tsai, Harrison Steel, Jakob Nicolaus Foerster:
ADIOS: Antibody Development via Opponent Shaping. ICML 2025 - [c95]Yoram Bachrach, Edan Toledo, Karen Hambardzumyan, Despoina Magka, Martin Josifoski, Minqi Jiang, Jakob N. Foerster, Roberta Raileanu, Tatiana Shavrina, Nicola Cancedda, Avraham Ruderman, Katie Millican, Andrei Lupu, Rishi Hazra:
Combining Code Generating Large Language Models and Self-Play to Iteratively Refine Strategies in Games. IJCAI 2025: 10999-11003 - [i154]Qizhen Zhang, Prajjwal Bhargava, Chloe Bi, Chris X. Cai, Jakob N. Foerster, Jeremy Fu, Punit Singh Koura, Ruan Silva, Sheng Shen, Emily Dinan, Suchin Gururangan, Mike Lewis:
BTS: Harmonizing Specialized Experts into a Generalist LLM. CoRR abs/2502.00075 (2025) - [i153]J. Rosser, Jakob Nicolaus Foerster:
AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds. CoRR abs/2502.00757 (2025) - [i152]Darius Muglich, Johannes Forkel, Elise van der Pol, Jakob N. Foerster:
Expected Return Symmetries. CoRR abs/2502.01711 (2025) - [i151]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. CoRR abs/2502.09172 (2025) - [i150]Thomas Foster, Jakob N. Foerster:
Learning to Reason at the Frontier of Learnability. CoRR abs/2502.12272 (2025) - [i149]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. CoRR abs/2502.14143 (2025) - [i148]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. CoRR abs/2502.14499 (2025) - [i147]Lucas Schorling, Pranav Vaidhyanathan, Jonas Schuff, Miguel J. Carballido, Dominik M. Zumbühl, Gerard Milburn, Florian Marquardt, Jakob N. Foerster, Michael A. Osborne, Natalia Ares:
Meta-learning characteristics and dynamics of quantum systems. CoRR abs/2503.10492 (2025) - [i146]Tobias Gessler, Tin Dizdarevic, Ani Calinescu, Benjamin Ellis, Andrei Lupu, Jakob Nicolaus Foerster:
OvercookedV2: Rethinking Overcooked for Zero-Shot Coordination. CoRR abs/2503.17821 (2025) - [i145]Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob N. Foerster, Jeff Clune, David Ha:
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search. CoRR abs/2504.08066 (2025) - [i144]Matthew Thomas Jackson, Uljad Berdica, Jarek Liesen, Shimon Whiteson, Jakob Nicolaus Foerster:
A Clean Slate for Offline Reinforcement Learning. CoRR abs/2504.11453 (2025) - [i143]Nathan Monette, Alistair Letcher, Michael Beukman, Matthew Thomas Jackson, Alexander Rutherford, Alexander David Goldie, Jakob N. Foerster:
An Optimisation Framework for Unsupervised Environment Design. CoRR abs/2505.20659 (2025) - [i142]Mattie Fellows, Clarisse Wibault, Uljad Berdica, Johannes Forkel, Michael A. Osborne, Jakob N. Foerster:
SOReL and TOReL: Two Methods for Fully Offline Reinforcement Learning. CoRR abs/2505.22442 (2025) - [i141]Anya Sims, Thom Foster, Klara Kaleb, Tuan-Duy H. Nguyen, Joseph Lee, Jakob N. Foerster, Yee Whye Teh, Cong Lu:
StochasTok: Improving Fine-Grained Subword Understanding in LLMs. CoRR abs/2506.01687 (2025) - [i140]Tim Franzmeyer, Archie Sravankumar, Lijuan Liu, Yuning Mao, Rui Hou, Sinong Wang, Jakob N. Foerster, Luke Zettlemoyer, Madian Khabsa:
High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning. CoRR abs/2506.04051 (2025) - [i139]Eltayeb Ahmed, Uljad Berdica, Martha Elliott, Danijela Horak, Jakob N. Foerster:
Intent Factored Generation: Unleashing the Diversity in Your Language Model. CoRR abs/2506.09659 (2025) - [i138]Jonathan Cook, Silvia Sapora, Arash Ahmadian, Akbir Khan, Tim Rocktäschel, Jakob N. Foerster, Laura Ruis:
Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training. CoRR abs/2506.18777 (2025) - [i137]Andrei Lupu, Timon Willi, Jakob N. Foerster:
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind. CoRR abs/2506.20664 (2025) - [i136]Tin Dizdarevic, Ravi Hammond, Tobias Gessler, Anisoara Calinescu, Jonathan Cook, Matteo Gallici, Andrei Lupu, Darius Muglich, Johannes Forkel, Jakob Nicolaus Foerster:
Ad-Hoc Human-AI Coordination Challenge. CoRR abs/2506.21490 (2025) - [i135]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, Edan Toledo, Karen Hambardzumyan, Martin Josifoski, Thomas Foster, Lucia Cipolina-Kun, Abhishek Charnalia, Derek Dunfield, Alexander H. Miller, Oisin Mac Aodha, Jakob N. Foerster, Yoram Bachrach:
The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements. CoRR abs/2506.22419 (2025) - [i134]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, Kelvin Niu, Tatiana Shavrina, Jean-Christophe Gagnon-Audet, Michael Shvartsman, Shagun Sodhani, Alexander H. Miller, Abhishek Charnalia, Derek Dunfield, Carole-Jean Wu, Pontus Stenetorp, Nicola Cancedda, Jakob Nicolaus Foerster, Yoram Bachrach:
AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench. CoRR abs/2507.02554 (2025) - [i133]Alexander David Goldie, Zilin Wang, Jakob Nicolaus Foerster, Shimon Whiteson:
How Should We Meta-Learn Reinforcement Learning Algorithms? CoRR abs/2507.17668 (2025) - [i132]Davide Paglieri, Bartlomiej Cupial, Jonathan Cook, Ulyana Piterbarg, Jens Tuyls, Edward Grefenstette, Jakob Nicolaus Foerster, Jack Parker-Holder, Tim Rocktäschel:
Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents. CoRR abs/2509.03581 (2025) - [i131]Ahmet Hamdi Güzel, Matthew Thomas Jackson, Jarek Liesen, Tim Rocktäschel, Jakob Nicolaus Foerster, Ilija Bogunovic, Jack Parker-Holder:
Imagined Autocurricula. CoRR abs/2509.13341 (2025) - [i130]Zheng Xiong, Kang Li, Zilin Wang, Matthew Thomas Jackson, Jakob N. Foerster, Shimon Whiteson:
HyperVLA: Efficient Inference in Vision-Language-Action Models via Hypernetworks. CoRR abs/2510.04898 (2025) - [i129]Valentin Mohl, Sascha Frey, Reuben Leyland, Kang Li, George Nigmatulin, Mihai Cucuringu, Stefan Zohren, Jakob N. Foerster, Anisoara Calinescu:
JaxMARL-HFT: GPU-Accelerated Large-Scale Multi-Agent Reinforcement Learning for High-Frequency Trading. CoRR abs/2511.02136 (2025) - [i128]Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou, Franziska Sofia Hafner, Harry Mayne, Jan Batzner, Negar Foroutan, Chris Schmitz, Karolina Korgul, Hunar Batra, Oishi Deb, Emma Beharry, Cornelius Emde, Thomas Foster, Anna Gausen, María Grandury, Simeng Han, Valentin Hofmann, Lujain Ibrahim, Hazel Kim, Hannah Rose Kirk, Fangru Lin, Gabrielle Kaili-May Liu, Lennart Luettgau, Jabez Magomere, Jonathan Rystrøm, Anna Sotnikova, Yushi Yang, Yilun Zhao, Adel Bibi, Antoine Bosselut, Ronald Clark, Arman Cohan, Jakob N. Foerster, Yarin Gal, Scott A. Hale, Inioluwa Deborah Raji, Christopher Summerfield, Philip H. S. Torr, Cozmin Ududec, Luc Rocher, Adam Mahdi:
Measuring what Matters: Construct Validity in Large Language Model Benchmarks. CoRR abs/2511.04703 (2025) - [i127]Bassel Al Omari, Michael T. Matthews, Alexander Rutherford, Jakob Nicolaus Foerster:
Multi-Agent Craftax: Benchmarking Open-Ended Multi-Agent Reinforcement Learning at the Hyperscale. CoRR abs/2511.04904 (2025) - [i126]Bidipta Sarkar, Mattie Fellows, Juan Agustin Duque, Alistair Letcher, Antonio León Villares, Anya Sims, Dylan Cope, Jarek Liesen, Lukas Seier, Theo Wolf, Uljad Berdica, Alexander David Goldie, Aaron C. Courville, Karin Sevegnani, Shimon Whiteson, Jakob Nicolaus Foerster:
Evolution Strategies at the Hyperscale. CoRR abs/2511.16652 (2025) - [i125]Johannes Forkel, Jakob N. Foerster:
Entropy is all you need for Inter-Seed Cross-Play in Hanabi. CoRR abs/2511.22581 (2025) - [i124]Joe Edelman, Tan Zhi-Xuan, Ryan Lowe, Oliver Klingefjord, Vincent Wang-Mascianica, Matija Franklin, Ryan Othniel Kearns, Ellie Hain, Atrisha Sarkar, Michiel A. Bakker, Fazl Barez, David Duvenaud, Jakob N. Foerster, Iason Gabriel, Joseph Gubbels, Bryce Goodman, Andreas Haupt, Jobst Heitzig, Julian Jara-Ettinger, Atoosa Kasirzadeh, James Ravi Kirkpatrick, Andrew Koh, W. Bradley Knox, Philipp E. Koralus, Joel Lehman, Sydney Levine, Samuele Marro, Manon Revel, Toby Shorin, Morgan Sutherland, Michael Henry Tessler, Ivan Vendrov, James Wilken-Smith:
Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value. CoRR abs/2512.03399 (2025) - [i123]Jason Weston, Jakob N. Foerster:
AI & Human Co-Improvement for Safer Co-Superintelligence. CoRR abs/2512.05356 (2025) - 2024
- [j9]Timon Willi, Johan S. Obando-Ceron, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Pablo Samuel Castro:
Mixture of Experts in a Mixture of RL settings. RLJ 3: 1072-1105 (2024) - [j8]Matthew Thomas Jackson, Michael T. Matthews, Cong Lu, Benjamin Ellis, Shimon Whiteson, Jakob Nicolaus Foerster:
Policy-Guided Diffusion. RLJ 4: 1855-1872 (2024) - [j7]Jessica James, Sebastian Towers, Jakob N. Foerster, Harrison Steel:
Optimisation strategies for directed evolution without sequencing. PLoS Comput. Biol. 20(12): 1012695 (2024) - [j6]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 Chenyu Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Aleksandar Petrov, Christian Schröder de Witt, Sumeet Ramesh Motwani, Yoshua Bengio, Danqi Chen, Philip Torr, Samuel Albanie, Tegan Maharaj, Jakob Nicolaus Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger:
Foundational Challenges in Assuring Alignment and Safety of Large Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c94]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 - [c93]Paul Barde, Jakob N. Foerster, Derek Nowrouzezahrai, Amy Zhang:
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem. AAMAS 2024: 141-150 - [c92]Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob N. Foerster:
Analysing the Sample Complexity of Opponent Shaping. AAMAS 2024: 623-631 - [c91]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: 1001-1010 - [c90]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: 1445-1453 - [c89]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: 2444-2446 - [c88]Tim Franzmeyer, Edith Elkind, Philip Torr, Jakob Nicolaus Foerster, João F. Henriques:
Select to Perfect: Imitating desired behavior from large multi-agent data. ICLR 2024 - [c87]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. ICLR 2024 - [c86]Matthew Thomas Jackson, Chris Lu, Louis Kirsch, Robert Tjarko Lange, Shimon Whiteson, Jakob Nicolaus Foerster:
Discovering Temporally-Aware Reinforcement Learning Algorithms. ICLR 2024 - [c85]Yat Long Lo, Biswa Sengupta, Jakob Nicolaus Foerster, Michael Noukhovitch:
Learning Multi-Agent Communication with Contrastive Learning. ICLR 2024 - [c84]Andrei Lupu, Chris Lu, Jarek Liesen, Robert Tjarko Lange, Jakob Nicolaus Foerster:
Behaviour Distillation. ICLR 2024 - [c83]Michael Beukman, Samuel Coward, Michael T. Matthews, Mattie Fellows, Minqi Jiang, Michael D. Dennis, Jakob Nicolaus Foerster:
Refining Minimax Regret for Unsupervised Environment Design. ICML 2024: 3637-3657 - [c82]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. ICML 2024: 12348-12370 - [c81]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. ICML 2024: 21577-21605 - [c80]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. ICML 2024: 35104-35137 - [c79]Johan S. 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. ICML 2024: 38520-38540 - [c78]Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster:
EvIL: Evolution Strategies for Generalisable Imitation Learning. ICML 2024: 43407-43421 - [c77]Ziyang Zhang, Qizhen Zhang, Jakob Nicolaus Foerster:
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition. ICML 2024: 60271-60287 - [c76]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. NeurIPS 2024 - [c75]Jonathan Cook, Chris Lu, Edward Hughes, Joel Z. Leibo, Jakob N. Foerster:
Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning. NeurIPS 2024 - [c74]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. NeurIPS 2024 - [c73]Alexander David Goldie, Chris Lu, Matthew Thomas Jackson, Shimon Whiteson, Jakob N. Foerster:
Can Learned Optimization Make Reinforcement Learning Less Difficult? NeurIPS 2024 - [c72]Steven D. Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob N. Foerster, Amanda Prorok:
Recurrent Reinforcement Learning with Memoroids. NeurIPS 2024 - [c71]Alexander Rutherford, Michael Beukman, Timon Willi, Bruno Lacerda, Nick Hawes, Jakob N. Foerster:
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery. NeurIPS 2024 - [c70]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. NeurIPS 2024 - [c69]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. NeurIPS 2024 - [c68]Anya Sims, Cong Lu, Jakob N. Foerster, Yee Whye Teh:
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning. NeurIPS 2024 - [c67]Rakshit S. 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. NeurIPS 2024 - [c66]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. NeurIPS 2024 - [c65]Uljad Berdica, Matthew Thomas Jackson, Niccolò Enrico Veronese, Jakob N. Foerster, Perla Maiolino:
Reinforcement Learning Controllers for Soft Robots Using Learned Environments. RoboSoft 2024: 933-939 - [c64]Samuel Sokota, Dylan Sam, Christian Schröder de Witt, Spencer Compton, Jakob N. Foerster, J. Zico Kolter:
Computing Low-Entropy Couplings for Large-Support Distributions. UAI 2024: 3279-3298 - [i122]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 abs/2402.01088 (2024) - [i121]Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob N. Foerster:
Analysing the Sample Complexity of Opponent Shaping. CoRR abs/2402.05782 (2024) - [i120]Matthew Thomas Jackson, Chris Lu, Louis Kirsch, Robert T. Lange, Shimon Whiteson, Jakob Nicolaus Foerster:
Discovering Temporally-Aware Reinforcement Learning Algorithms. CoRR abs/2402.05828 (2024) - [i119]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 abs/2402.08609 (2024) - [i118]Steven D. Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob N. Foerster, Amanda Prorok:
Revisiting Recurrent Reinforcement Learning with Memory Monoids. CoRR abs/2402.09900 (2024) - [i117]Ravi Hammond, Dustin Craggs, Mingyu Guo, Jakob N. Foerster, Ian D. Reid:
Symmetry-Breaking Augmentations for Ad Hoc Teamwork. CoRR abs/2402.09984 (2024) - [i116]Michael Beukman, Samuel Coward, Michael T. Matthews, Mattie Fellows, Minqi Jiang, Michael Dennis, Jakob N. Foerster:
Refining Minimax Regret for Unsupervised Environment Design. CoRR abs/2402.12284 (2024) - [i115]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. CoRR abs/2402.16801 (2024) - [i114]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 abs/2402.16822 (2024) - [i113]Samuel Coward, Michael Beukman, Jakob N. Foerster:
JaxUED: A simple and useable UED library in Jax. CoRR abs/2403.13091 (2024) - [i112]Matthew Thomas Jackson, Michael T. Matthews, Cong Lu, Benjamin Ellis, Shimon Whiteson, Jakob N. Foerster:
Policy-Guided Diffusion. CoRR abs/2404.06356 (2024) - [i111]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 abs/2404.07099 (2024) - [i110]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 C. 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 abs/2404.09932 (2024) - [i109]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. CoRR abs/2404.17047 (2024) - [i108]Tim Franzmeyer, Edith Elkind, Philip Torr, Jakob N. Foerster, João F. Henriques:
Select to Perfect: Imitating desired behavior from large multi-agent data. CoRR abs/2405.03735 (2024) - [i107]Ziyang Zhang, Qizhen Zhang, Jakob N. Foerster:
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition. CoRR abs/2405.07932 (2024) - [i106]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. CoRR abs/2405.08597 (2024) - [i105]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 abs/2405.19540 (2024) - [i104]Jonathan Cook, Chris Lu, Edward Hughes, Joel Z. Leibo, Jakob N. Foerster:
Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning. CoRR abs/2406.00392 (2024) - [i103]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 abs/2406.03428 (2024) - [i102]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 abs/2406.08414 (2024) - [i101]Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster:
EvIL: Evolution Strategies for Generalisable Imitation Learning. CoRR abs/2406.11905 (2024) - [i100]Jarek Liesen, Chris Lu, Andrei Lupu, Jakob N. Foerster, Henning Sprekeler, Robert T. Lange:
Discovering Minimal Reinforcement Learning Environments. CoRR abs/2406.12589 (2024) - [i99]Andrei Lupu, Chris Lu, Jarek Liesen, Robert Tjarko Lange, Jakob N. Foerster:
Behaviour Distillation. CoRR abs/2406.15042 (2024) - [i98]Timon Willi, Johan S. Obando-Ceron, Jakob N. Foerster, Karolina Dziugaite, Pablo Samuel Castro:
Mixture of Experts in a Mixture of RL settings. CoRR abs/2406.18420 (2024) - [i97]Matteo Gallici, Mattie Fellows, Benjamin Ellis, Bartomeu Pou, Ivan Masmitja, Jakob Nicolaus Foerster, Mario Martin:
Simplifying Deep Temporal Difference Learning. CoRR abs/2407.04811 (2024) - [i96]Alexander David Goldie, Chris Lu, Matthew Thomas Jackson, Shimon Whiteson, Jakob Nicolaus Foerster:
Can Learned Optimization Make Reinforcement Learning Less Difficult? CoRR abs/2407.07082 (2024) - [i95]Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob N. Foerster, Jeff Clune, David Ha:
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery. CoRR abs/2408.06292 (2024) - [i94]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. CoRR abs/2408.08274 (2024) - [i93]Alexander Rutherford, Michael Beukman, Timon Willi, Bruno Lacerda, Nick Hawes, Jakob N. Foerster:
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery. CoRR abs/2408.15099 (2024) - [i92]Chris Lu, Michael Beukman, Michael T. Matthews, Jakob N. Foerster:
JaxLife: An Open-Ended Agentic Simulator. CoRR abs/2409.00853 (2024) - [i91]Alisia Maria Lupidi, Carlos Gemmell, Nicola Cancedda, Jane Dwivedi-Yu, Jason Weston, Jakob N. Foerster, Roberta Raileanu, Maria Lomeli:
Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources. CoRR abs/2409.08239 (2024) - [i90]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. CoRR abs/2409.10588 (2024) - [i89]Jonathan Cook, Tim Rocktäschel, Jakob N. Foerster, Dennis Aumiller, Alex Wang:
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation. CoRR abs/2410.03608 (2024) - [i88]Uljad Berdica, Matthew Thomas Jackson, Niccolò Enrico Veronese, Jakob N. Foerster, Perla Maiolino:
Reinforcement Learning Controllers for Soft Robots using Learned Environments. CoRR abs/2410.18519 (2024) - [i87]Lize Alberts, Benjamin Ellis, Andrei Lupu, Jakob N. Foerster:
CURATe: Benchmarking Personalised Alignment of Conversational AI Assistants. CoRR abs/2410.21159 (2024) - [i86]Michael T. Matthews, Michael Beukman, Chris Lu, Jakob N. Foerster:
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks. CoRR abs/2410.23208 (2024) - [i85]Charlie B. Tan, Edan Toledo, Benjamin Ellis, Jakob N. Foerster, Ferenc Huszár:
Beyond the Boundaries of Proximal Policy Optimization. CoRR abs/2411.00666 (2024) - [i84]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. CoRR abs/2411.04976 (2024) - [i83]Carlo Alfano, Silvia Sapora, Jakob Nicolaus Foerster, Patrick Rebeschini, Yee Whye Teh:
Learning Loss Landscapes in Preference Optimization. CoRR abs/2411.06568 (2024) - [i82]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. CoRR abs/2411.13543 (2024) - [i81]Branton DeMoss, Silvia Sapora, Jakob N. Foerster, Nick Hawes, Ingmar Posner:
The Complexity Dynamics of Grokking. CoRR abs/2412.09810 (2024) - [i80]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. CoRR abs/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) - [c63]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: 91-99 - [c62]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: 583-591 - [c61]Brandon Cui, Andrei Lupu, Samuel Sokota, Hengyuan Hu, David J. Wu, Jakob Nicolaus Foerster:
Adversarial Diversity in Hanabi. ICLR 2023 - [c60]Yat Long Lo, Christian Schröder de Witt, Samuel Sokota, Jakob Nicolaus Foerster, Shimon Whiteson:
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning. ICLR 2023 - [c59]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. ICLR 2023 - [c58]Christian Schröder de Witt, Samuel Sokota, J. Zico Kolter, Jakob Nicolaus Foerster, Martin Strohmeier:
Perfectly Secure Steganography Using Minimum Entropy Coupling. ICLR 2023 - [c57]Chris Lu, Timon Willi, Alistair Letcher, Jakob Nicolaus Foerster:
Adversarial Cheap Talk. ICML 2023: 22917-22941 - [c56]Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Nicolaus Foerster:
Learning Intuitive Policies Using Action Features. ICML 2023: 23358-23372 - [c55]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 - [c54]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 - [c53]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 - [c52]Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster:
Similarity-based cooperative equilibrium. NeurIPS 2023 - [i79]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 abs/2303.03376 (2023) - [i78]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. CoRR abs/2303.03982 (2023) - [i77]Chris Lu, Sebastian Towers, Jakob N. Foerster:
Arbitrary Order Meta-Learning with Simple Population-Based Evolution. CoRR abs/2303.09478 (2023) - [i76]Yat Long Lo, Christian Schröder de Witt, Samuel Sokota, Jakob Nicolaus Foerster, Shimon Whiteson:
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning. CoRR abs/2303.10733 (2023) - [i75]Paul Barde, Jakob N. Foerster, Derek Nowrouzezahrai, Amy Zhang:
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem. CoRR abs/2305.17198 (2023) - [i74]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. CoRR abs/2306.01460 (2023) - [i73]Yat Long Lo, Biswa Sengupta, Jakob N. Foerster, Michael Noukhovitch:
Learning to Communicate using Contrastive Learning. CoRR abs/2307.01403 (2023) - [i72]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. CoRR abs/2308.08051 (2023) - [i71]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 abs/2308.13289 (2023) - [i70]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 abs/2309.00638 (2023) - [i69]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. CoRR abs/2310.02782 (2023) - [i68]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. CoRR abs/2311.10090 (2023) - [i67]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 abs/2312.12568 (2023) - 2022
- [c51]Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob N. Foerster:
Lyapunov Exponents for Diversity in Differentiable Games. AAMAS 2022: 842-852 - [c50]Qizhen Zhang, Christopher Lu, Animesh Garg, Jakob N. Foerster:
Centralized Model and Exploration Policy for Multi-Agent RL. AAMAS 2022: 1500-1508 - [c49]Samuel Sokota, Hengyuan Hu, David J. Wu, J. Zico Kolter, Jakob Nicolaus Foerster, Noam Brown:
A Fine-Tuning Approach to Belief State Modeling. ICLR 2022 - [c48]Jakub Grudzien Kuba, Christian A. Schröder de Witt, Jakob N. Foerster:
Mirror Learning: A Unifying Framework of Policy Optimisation. ICML 2022: 7825-7844 - [c47]Christopher Lu, Timon Willi, Christian A. Schröder de Witt, Jakob N. Foerster:
Model-Free Opponent Shaping. ICML 2022: 14398-14411 - [c46]Darius Muglich, Luisa M. Zintgraf, Christian A. Schröder de Witt, Shimon Whiteson, Jakob N. Foerster:
Generalized Beliefs for Cooperative AI. ICML 2022: 16062-16082 - [c45]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: 17473-17498 - [c44]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: 20314-20328 - [c43]Timon Willi, Alistair Letcher, Johannes Treutlein, Jakob N. Foerster:
COLA: Consistent Learning with Opponent-Learning Awareness. ICML 2022: 23804-23831 - [c42]Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schröder de Witt, Jakob N. Foerster:
Discovered Policy Optimisation. NeurIPS 2022 - [c41]Brandon Cui, Hengyuan Hu, Andrei Lupu, Samuel Sokota, Jakob N. Foerster:
Off-Team Learning. NeurIPS 2022 - [c40]Hengyuan Hu, Samuel Sokota, David J. Wu, Anton Bakhtin, Andrei Lupu, Brandon Cui, Jakob N. Foerster:
Self-Explaining Deviations for Coordination. NeurIPS 2022 - [c39]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 - [c38]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 - [c37]Darius Muglich, Christian Schröder de Witt, Elise van der Pol, Shimon Whiteson, Jakob N. Foerster:
Equivariant Networks for Zero-Shot Coordination. NeurIPS 2022 - [c36]Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, Jakob N. Foerster:
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world. NeurIPS 2022 - [c35]Stephen Zhao, Chris Lu, Roger B. Grosse, Jakob N. Foerster:
Proximal Learning With Opponent-Learning Awareness. NeurIPS 2022 - [i66]Jakub Grudzien Kuba, Christian Schröder de Witt, Jakob N. Foerster:
Mirror Learning: A Unifying Framework of Policy Optimisation. CoRR abs/2201.02373 (2022) - [i65]Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob N. Foerster:
Learning to Coordinate with Humans using Action Features. CoRR abs/2201.12658 (2022) - [i64]Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel:
Evolving Curricula with Regret-Based Environment Design. CoRR abs/2203.01302 (2022) - [i63]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 abs/2203.03535 (2022) - [i62]Timon Willi, Johannes Treutlein, Alistair Letcher, Jakob N. Foerster:
COLA: Consistent Learning with Opponent-Learning Awareness. CoRR abs/2203.04098 (2022) - [i61]Christopher Lu, Timon Willi, Christian Schröder de Witt, Jakob N. Foerster:
Model-Free Opponent Shaping. CoRR abs/2205.01447 (2022) - [i60]Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, Jakob N. Foerster:
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world. CoRR abs/2206.09889 (2022) - [i59]Darius Muglich, Luisa M. Zintgraf, Christian Schröder de Witt, Shimon Whiteson, Jakob N. Foerster:
Generalized Beliefs for Cooperative AI. CoRR abs/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. CoRR abs/2207.05219 (2022) - [i57]Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob N. Foerster:
K-level Reasoning for Zero-Shot Coordination in Hanabi. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/2210.05125 (2022) - [i52]Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schröder de Witt, Jakob N. Foerster:
Discovered Policy Optimisation. CoRR abs/2210.05639 (2022) - [i51]Isaac Liao, Rumen R. Dangovski, Jakob N. Foerster, Marin Soljacic:
Learning to Optimize Quasi-Newton Methods. CoRR abs/2210.06171 (2022) - [i50]Stephen Zhao, Chris Lu, Roger Baker Grosse, Jakob Nicolaus Foerster:
Proximal Learning With Opponent-Learning Awareness. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/2210.16175 (2022) - [i46]Chris Lu, Timon Willi, Alistair Letcher, Jakob N. Foerster:
Adversarial Cheap Talk. CoRR abs/2211.11030 (2022) - [i45]Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster:
Similarity-based Cooperation. CoRR abs/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. CoRR abs/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. AAMAS 2021: 1593-1595 - [c33]Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, Noam Brown, Jakob N. Foerster:
Off-Belief Learning. ICML 2021: 4369-4379 - [c32]Andrei Lupu, Brandon Cui, Hengyuan Hu, Jakob N. Foerster:
Trajectory Diversity for Zero-Shot Coordination. ICML 2021: 7204-7213 - [c31]Johannes Treutlein, Michael Dennis, Caspar Oesterheld, Jakob N. Foerster:
A New Formalism, Method and Open Issues for Zero-Shot Coordination. ICML 2021: 10413-10423 - [c30]Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel:
Replay-Guided Adversarial Environment Design. NeurIPS 2021: 1884-1897 - [c29]Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster:
Neural Pseudo-Label Optimism for the Bank Loan Problem. NeurIPS 2021: 6580-6593 - [c28]Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob N. Foerster:
K-level Reasoning for Zero-Shot Coordination in Hanabi. NeurIPS 2021: 8215-8228 - [i43]Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, David J. Wu, Noam Brown, Jakob N. Foerster:
Off-Belief Learning. CoRR abs/2103.04000 (2021) - [i42]Kalesha Bullard, Douwe Kiela, Joelle Pineau, Jakob N. Foerster:
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication. CoRR abs/2103.08067 (2021) - [i41]Johannes Treutlein, Michael Dennis, Caspar Oesterheld, Jakob N. Foerster:
A New Formalism, Method and Open Issues for Zero-Shot Coordination. CoRR abs/2106.06613 (2021) - [i40]Hengyuan Hu, Adam Lerer, Noam Brown, Jakob N. Foerster:
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings. CoRR abs/2106.09086 (2021) - [i39]Qizhen Zhang, Christopher Lu, Animesh Garg, Jakob N. Foerster:
Centralized Model and Exploration Policy for Multi-Agent RL. CoRR abs/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. CoRR abs/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. CoRR abs/2107.12460 (2021) - [i36]Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel:
Replay-Guided Adversarial Environment Design. CoRR abs/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. CoRR abs/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. CoRR abs/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. AAAI 2020: 3243-3250 - [c26]Adam Lerer, Hengyuan Hu, Jakob N. Foerster, Noam Brown:
Improving Policies via Search in Cooperative Partially Observable Games. AAAI 2020: 7187-7194 - [c25]Cinjon Resnick, Abhinav Gupta, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho:
Capacity, Bandwidth, and Compositionality in Emergent Language Learning. AAMAS 2020: 1125-1133 - [c24]Hengyuan Hu, Jakob N. Foerster:
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning. ICLR 2020 - [c23]Ryan Lowe, Abhinav Gupta, Jakob N. Foerster, Douwe Kiela, Joelle Pineau:
On the interaction between supervision and self-play in emergent communication. ICLR 2020 - [c22]Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob N. Foerster:
"Other-Play" for Zero-Shot Coordination. ICML 2020: 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. NeurIPS 2020 - [c20]Abhinav Gupta, Cinjon Resnick, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho:
Compositionality and Capacity in Emergent Languages. RepL4NLP@ACL 2020: 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. CoRR abs/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. CoRR abs/2002.04676 (2020) - [i31]Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob N. Foerster:
"Other-Play" for Zero-Shot Coordination. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/2011.06505 (2020)
2010 – 2019
- 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. AAMAS 2019: 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. AAMAS 2019: 2186-2188 - [c17]Abhinav Gupta, Ryan Lowe, Jakob N. Foerster, Douwe Kiela, Joelle Pineau:
Seeded self-play for language learning. LANTERN@EMNLP-IJCNLP 2019: 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. ICML 2019: 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. ICML 2019: 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. IJCAI 2019: 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. NeurIPS 2019: 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. NeurIPS 2019: 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. CoRR abs/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. CoRR abs/1902.04043 (2019) - [i24]Ryan Lowe, Jakob N. Foerster, Y-Lan Boureau, Joelle Pineau, Yann N. Dauphin:
On the Pitfalls of Measuring Emergent Communication. CoRR abs/1903.05168 (2019) - [i23]Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
Differentiable Game Mechanics. CoRR abs/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. CoRR abs/1906.03926 (2019) - [i21]Thomas D. Barrett, William R. Clements, Jakob N. Foerster, A. I. Lvovsky:
Exploratory Combinatorial Optimization with Reinforcement Learning. CoRR abs/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. CoRR abs/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. CoRR abs/1910.02065 (2019) - [i18]Reda Bahi Slaoui, William R. Clements, Jakob N. Foerster, Sébastien Toth:
Robust Domain Randomization for Reinforcement Learning. CoRR abs/1910.10537 (2019) - [i17]Cinjon Resnick, Abhinav Gupta, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho:
Capacity, Bandwidth, and Compositionality in Emergent Language Learning. CoRR abs/1910.11424 (2019) - [i16]Hengyuan Hu, Jakob N. Foerster:
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning. CoRR abs/1912.02288 (2019) - [i15]Adam Lerer, Hengyuan Hu, Jakob N. Foerster, Noam Brown:
Improving Policies via Search in Cooperative Partially Observable Games. CoRR abs/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. AAAI 2018: 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 Workshops 2018 - [c8]Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch:
Learning with Opponent-Learning Awareness. AAMAS 2018: 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. ICML 2018: 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. ICML 2018: 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. ICML 2018: 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. CoRR abs/1802.05098 (2018) - [i13]David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
The Mechanics of n-Player Differentiable Games. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/1811.01458 (2018) - [i8]Alistair Letcher, Jakob N. Foerster, David Balduzzi, Tim Rocktäschel, Shimon Whiteson:
Stable Opponent Shaping in Differentiable Games. CoRR abs/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. ICML 2017: 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. ICML 2017: 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. CoRR abs/1702.08887 (2017) - [i6]Jakob N. Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, Shimon Whiteson:
Counterfactual Multi-Agent Policy Gradients. CoRR abs/1705.08926 (2017) - [i5]Christoph Aymanns, Jakob N. Foerster, Co-Pierre Georg:
Fake News in Social Networks. CoRR abs/1708.06233 (2017) - [i4]Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch:
Learning with Opponent-Learning Awareness. CoRR abs/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. NIPS 2016: 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. CoRR abs/1602.02672 (2016) - [i2]Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson:
Learning to Communicate with Deep Multi-Agent Reinforcement Learning. CoRR abs/1605.06676 (2016) - [i1]Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo:
Intelligible Language Modeling with Input Switched Affine Networks. CoRR abs/1611.09434 (2016)
Coauthor Index
Ani Calinescu
aka: Anisoara Calinescu
aka: Anisoara Calinescu
Michael Dennis 0001
aka: Michael D. Dennis
aka: Michael D. Dennis
Robert T. Lange
aka: Robert Tjarko Lange
aka: Robert Tjarko Lange
Philip Torr 0001
aka: Philip H. S. Torr
aka: Philip H. S. Torr
[i144] [i133] [i130] [i126] [j8] [c89] [c86] [c74] [c73] [c70] [i120] [i112] [i96] [i80] [c60] [c54] [c53] [i76] [i69] [i68] [c46] [c44] [c37] [i59] [i54] [i49] [i44] [j4] [i38] [j2] [i32] [i30] [c18] [c16] [c15] [c14] [c13] [c12] [c11] [i25] [i22] [i20] [c10] [c8] [c7] [c5] [c4] [i14] [i12] [i10] [i9] [i8] [c2] [i7] [i6] [i4] [c1] [i3] [i2]
Christian Schröder de Witt
aka: Christian A. Schröder de Witt
aka: Christian A. Schröder de Witt
Qizhen Zhang 0002
aka: Qizhen (Irene) Zhang
aka: Qizhen (Irene) Zhang
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