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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
- 2024
[c80]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- [c79]Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob N. Foerster:
Analysing the Sample Complexity of Opponent Shaping. AAMAS 2024: 623-631 - [c78]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 - [c77]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 - [c76]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 - [c75]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 - [c74]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 - [c73]Matthew Thomas Jackson, Chris Lu, Louis Kirsch, Robert Tjarko Lange, Shimon Whiteson, Jakob Nicolaus Foerster:
Discovering Temporally-Aware Reinforcement Learning Algorithms. ICLR 2024 - [c72]Yat Long Lo, Biswa Sengupta, Jakob Nicolaus Foerster, Michael Noukhovitch:
Learning Multi-Agent Communication with Contrastive Learning. ICLR 2024 - [c71]Andrei Lupu, Chris Lu, Jarek Liesen, Robert Tjarko Lange, Jakob Nicolaus Foerster:
Behaviour Distillation. ICLR 2024 - [c70]Michael Beukman, Samuel Coward, Michael Matthews, Mattie Fellows, Minqi Jiang, Michael D. Dennis, Jakob Nicolaus Foerster:
Refining Minimax Regret for Unsupervised Environment Design. ICML 2024 - [c69]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 - [c68]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 - [c67]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 - [c66]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. ICML 2024 - [c65]Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster:
EvIL: Evolution Strategies for Generalisable Imitation Learning. ICML 2024 - [c64]Ziyang Zhang, Qizhen Zhang, Jakob Nicolaus Foerster:
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition. ICML 2024 - [c63]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 - [i107]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) - [i106]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) - [i105]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) - [i104]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) - [i103]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) - [i102]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) - [i101]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) - [i100]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) - [i99]Samuel Coward, Michael Beukman, Jakob N. Foerster:
JaxUED: A simple and useable UED library in Jax. CoRR abs/2403.13091 (2024) - [i98]Matthew Thomas Jackson, Michael T. Matthews, Cong Lu, Benjamin Ellis, Shimon Whiteson, Jakob N. Foerster:
Policy-Guided Diffusion. CoRR abs/2404.06356 (2024) - [i97]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) - [i96]Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger:
Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR abs/2404.09932 (2024) - [i95]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) - [i94]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) - [i93]Ziyang Zhang, Qizhen Zhang, Jakob N. Foerster:
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition. CoRR abs/2405.07932 (2024) - [i92]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) - [i91]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) - [i90]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) - [i89]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) - [i88]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) - [i87]Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster:
EvIL: Evolution Strategies for Generalisable Imitation Learning. CoRR abs/2406.11905 (2024) - [i86]Jarek Liesen, Chris Lu, Andrei Lupu, Jakob N. Foerster, Henning Sprekeler, Robert T. Lange:
Discovering Minimal Reinforcement Learning Environments. CoRR abs/2406.12589 (2024) - [i85]Andrei Lupu, Chris Lu, Jarek Liesen, Robert Tjarko Lange, Jakob N. Foerster:
Behaviour Distillation. CoRR abs/2406.15042 (2024) - [i84]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) - [i83]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) - [i82]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) - [i81]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) - [i80]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) - [i79]Chris Lu, Michael Beukman, Michael T. Matthews, Jakob N. Foerster:
JaxLife: An Open-Ended Agentic Simulator. CoRR abs/2409.00853 (2024) - [i78]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) - 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. ICAIF 2023: 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. ICAIF 2023: 583-591 - [c60]Brandon Cui, Andrei Lupu, Samuel Sokota, Hengyuan Hu, David J. Wu, Jakob Nicolaus Foerster:
Adversarial Diversity in Hanabi. ICLR 2023 - [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. ICLR 2023 - [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. ICLR 2023 - [c57]Christian Schröder de Witt, Samuel Sokota, J. Zico Kolter, Jakob Nicolaus Foerster, Martin Strohmeier:
Perfectly Secure Steganography Using Minimum Entropy Coupling. ICLR 2023 - [c56]Chris Lu, Timon Willi, Alistair Letcher, Jakob Nicolaus Foerster:
Adversarial Cheap Talk. ICML 2023: 22917-22941 - [c55]Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Nicolaus Foerster:
Learning Intuitive Policies Using Action Features. ICML 2023: 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. NeurIPS 2023 - [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. NeurIPS 2023 - [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. NeurIPS 2023 - [c51]Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster:
Similarity-based cooperative equilibrium. NeurIPS 2023 - [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. CoRR abs/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. CoRR abs/2303.03982 (2023) - [i75]Chris Lu, Sebastian Towers, Jakob N. Foerster:
Arbitrary Order Meta-Learning with Simple Population-Based Evolution. CoRR abs/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. CoRR abs/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. CoRR abs/2306.01460 (2023) - [i72]Yat Long Lo, Biswa Sengupta, Jakob N. Foerster, Michael Noukhovitch:
Learning to Communicate using Contrastive Learning. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/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. CoRR abs/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. AAMAS 2022: 842-852 - [c49]Qizhen Zhang, Christopher Lu, Animesh Garg, Jakob N. Foerster:
Centralized Model and Exploration Policy for Multi-Agent RL. AAMAS 2022: 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. ICLR 2022 - [c47]Jakub Grudzien Kuba, Christian A. Schröder de Witt, Jakob N. Foerster:
Mirror Learning: A Unifying Framework of Policy Optimisation. ICML 2022: 7825-7844 - [c46]Christopher Lu, Timon Willi, Christian A. Schröder de Witt, Jakob N. Foerster:
Model-Free Opponent Shaping. ICML 2022: 14398-14411 - [c45]Darius Muglich, Luisa M. Zintgraf, Christian A. Schröder de Witt, Shimon Whiteson, Jakob N. Foerster:
Generalized Beliefs for Cooperative AI. ICML 2022: 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. ICML 2022: 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. ICML 2022: 20314-20328 - [c42]Timon Willi, Alistair Letcher, Johannes Treutlein, Jakob N. Foerster:
COLA: Consistent Learning with Opponent-Learning Awareness. ICML 2022: 23804-23831 - [c41]Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schröder de Witt, Jakob N. Foerster:
Discovered Policy Optimisation. NeurIPS 2022 - [c40]Brandon Cui, Hengyuan Hu, Andrei Lupu, Samuel Sokota, Jakob N. Foerster:
Off-Team Learning. NeurIPS 2022 - [c39]Hengyuan Hu, Samuel Sokota, David J. Wu, Anton Bakhtin, Andrei Lupu, Brandon Cui, Jakob N. Foerster:
Self-Explaining Deviations for Coordination. NeurIPS 2022 - [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. NeurIPS 2022 - [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. NeurIPS 2022 - [c36]Darius Muglich, Christian Schröder de Witt, Elise van der Pol, Shimon Whiteson, Jakob N. Foerster:
Equivariant Networks for Zero-Shot Coordination. NeurIPS 2022 - [c35]Stephen Zhao, Chris Lu, Roger B. Grosse, Jakob N. Foerster:
Proximal Learning With Opponent-Learning Awareness. NeurIPS 2022 - [i65]Jakub Grudzien Kuba, Christian Schröder de Witt, Jakob N. Foerster:
Mirror Learning: A Unifying Framework of Policy Optimisation. CoRR abs/2201.02373 (2022) - [i64]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) - [i63]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) - [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. CoRR abs/2203.03535 (2022) - [i61]Timon Willi, Johannes Treutlein, Alistair Letcher, Jakob N. Foerster:
COLA: Consistent Learning with Opponent-Learning Awareness. CoRR abs/2203.04098 (2022) - [i60]Christopher Lu, Timon Willi, Christian Schröder de Witt, Jakob N. Foerster:
Model-Free Opponent Shaping. CoRR abs/2205.01447 (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
Michael Dennis 0001
aka: Michael D. Dennis
aka: Michael D. Dennis
Philip Torr 0001
aka: Philip H. S. Torr
aka: Philip H. S. Torr
Christian Schröder de Witt
aka: Christian A. Schröder de Witt
aka: Christian A. Schröder de Witt
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