Joel Z. Leibo

According to our database, Joel Z. Leibo authored at least 81 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Visualizing 2x2 Normal-Form Games: twoxtwogame LaTeX Package.

Luke Marris, Ian Gemp, Siqi Liu, Joel Z. Leibo, Georgios Piliouras

CoRR, 2024

Neural Population Learning beyond Symmetric Zero-Sum Games.

Siqi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

2023

A social path to human-like artificial intelligence.

Edgar A. Duéñez-Guzmán, Suzanne Sadedin, Jane X. Wang, Kevin R. McKee, Joel Z. Leibo

Nat. Mac. Intell., October, 2023

The Puzzle of Evaluating Moral Cognition in Artificial Agents.

Madeline G. Reinecke, Yiran Mao, Markus Kunesch, Edgar A. Duéñez-Guzmán, Julia Haas, Joel Z. Leibo

Cogn. Sci., August, 2023

A Review of Cooperation in Multi-agent Learning.

Yali Du, Joel Z. Leibo, Usman Islam, Richard Willis, Peter Sunehag

CoRR, 2023

Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia.

Alexander Sasha Vezhnevets, John P. Agapiou, Avia Aharon, Ron Ziv, Jayd Matyas, Edgar A. Duéñez-Guzmán, William A. Cunningham, Simon Osindero, Danny Karmon, Joel Z. Leibo

CoRR, 2023

Machine Culture.

Levin Brinkmann, Fabian Baumann, Jean-François Bonnefon, Maxime Derex, Thomas F. Müller, Anne-Marie Nussberger, Agnieszka Czaplicka, Alberto Acerbi, Thomas L. Griffiths, Joseph Henrich, Joel Z. Leibo, Richard McElreath, Pierre-Yves Oudeyer, Jonathan Stray, Iyad Rahwan

CoRR, 2023

Resolving social dilemmas with minimal reward transfer.

Richard Willis, Yali Du, Joel Z. Leibo, Michael Luck

CoRR, 2023

Framework-Based Qualitative Analysis of Free Responses of Large Language Models: Algorithmic Fidelity.

Aliya Amirova, Theodora Fteropoulli, Nafiso Ahmed, Martin R. Cowie, Joel Z. Leibo

CoRR, 2023

Doing the right thing for the right reason: Evaluating artificial moral cognition by probing cost insensitivity.

Yiran Mao, Madeline G. Reinecke, Markus Kunesch, Edgar A. Duéñez-Guzmán, Ramona Comanescu, Julia Haas, Joel Z. Leibo

CoRR, 2023

Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas.

Udari Madhushani, Kevin R. McKee, John P. Agapiou, Joel Z. Leibo, Richard Everett, Thomas W. Anthony, Edward Hughes, Karl Tuyls, Edgar A. Duéñez-Guzmán

CoRR, 2023

Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition.

Peter Sunehag, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Igor Mordatch, Joel Z. Leibo

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

Meta-control of social learning strategies.

Anil Yaman, Nicolas Bredèche, Onur Çaylak, Joel Z. Leibo, Sang Wan Lee

PLoS Comput. Biol., 2022

Importance of prefrontal meta control in human-like reinforcement learning.

Jee Hang Lee, Joel Z. Leibo, Su Jin An, Sang Wan Lee

Frontiers Comput. Neurosci., 2022

Melting Pot 2.0.

John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Jayd Matyas, Yiran Mao, Peter Sunehag, Raphael Köster, Udari Madhushani, Kavya Kopparapu, Ramona Comanescu, DJ Strouse, Michael Bradley Johanson, Sukhdeep Singh, Julia Haas, Igor Mordatch, Dean Mobbs, Joel Z. Leibo

CoRR, 2022

The emergence of division of labor through decentralized social sanctioning.

Anil Yaman, Joel Z. Leibo, Giovanni Iacca, Sang Wan Lee

CoRR, 2022

Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning.

Michael Bradley Johanson, Edward Hughes, Finbarr Timbers, Joel Z. Leibo

CoRR, 2022

Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria.

Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas, Alexander Sasha Vezhnevets, John P. Agapiou, Kevin R. McKee, Richard Everett, Janusz Marecki, Joel Z. Leibo, Thore Graepel

CoRR, 2022

Quantifying the effects of environment and population diversity in multi-agent reinforcement learning.

Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett

Auton. Agents Multi Agent Syst., 2022

2021

Statistical discrimination in learning agents.

Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao, Ben Coppin, Silvia Chiappa, Alexander Sasha Vezhnevets, Michiel A. Bakker, Yoram Bachrach, Suzanne Sadedin, William Isaac, Karl Tuyls, Joel Z. Leibo

CoRR, 2021

A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings.

Eugene Vinitsky, Raphael Köster, John P. Agapiou, Edgar A. Duéñez-Guzmán, Alexander Sasha Vezhnevets, Joel Z. Leibo

CoRR, 2021

Deep reinforcement learning models the emergent dynamics of human cooperation.

Kevin R. McKee, Edward Hughes, Tina O. Zhu, Martin J. Chadwick, Raphael Koster, Antonio García Castañeda, Charlie Beattie, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo

CoRR, 2021

Quantifying environment and population diversity in multi-agent reinforcement learning.

Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett

CoRR, 2021

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot.

Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Alexander Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, Thore Graepel

Proceedings of the 38th International Conference on Machine Learning, 2021

Modelling Cooperation in Network Games with Spatio-Temporal Complexity.

Michiel A. Bakker, Richard Everett, Laura Weidinger, Iason Gabriel, William S. Isaac, Joel Z. Leibo, Edward Hughes

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020

Open Problems in Cooperative AI.

Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, Thore Graepel

CoRR, 2020

DeepMind Lab2D.

Charles Beattie, Thomas Köppe, Edgar A. Duéñez-Guzmán, Joel Z. Leibo

CoRR, 2020

Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences.

Raphael Köster, Kevin R. McKee, Richard Everett, Laura Weidinger, William S. Isaac, Edward Hughes, Edgar A. Duéñez-Guzmán, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo

CoRR, 2020

Negotiating team formation using deep reinforcement learning.

Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel

Artif. Intell., 2020

Bounds and dynamics for empirical game theoretic analysis.

Karl Tuyls, Julien Pérolat, Marc Lanctot, Edward Hughes, Richard Everett, Joel Z. Leibo, Csaba Szepesvári, Thore Graepel

Auton. Agents Multi Agent Syst., 2020

OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning.

Alexander Vezhnevets, Yuhuai Wu, Maria K. Eckstein, Rémi Leblond, Joel Z. Leibo

Proceedings of the 37th International Conference on Machine Learning, 2020

Smooth markets: A basic mechanism for organizing gradient-based learners.

David Balduzzi, Wojciech M. Czarnecki, Tom Anthony, Ian Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel

Proceedings of the 8th International Conference on Learning Representations, 2020

Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning.

Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

Silly Rules Improve the Capacity of Agents to Learn Stable Enforcement and Compliance Behaviors.

Raphael Koster, Dylan Hadfield-Menell, Gillian K. Hadfield, Joel Z. Leibo

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games.

Edward Hughes, Thomas W. Anthony, Tom Eccles, Joel Z. Leibo, David Balduzzi, Yoram Bachrach

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019

Toward high-performance, memory-efficient, and fast reinforcement learning - Lessons from decision neuroscience.

Ben Seymour, Joel Z. Leibo, Su Jin An, Sang Wan Lee

Sci. Robotics, 2019

Options as responses: Grounding behavioural hierarchies in multi-agent RL.

Alexander Sasha Vezhnevets, Yuhuai Wu, Rémi Leblond, Joel Z. Leibo

CoRR, 2019

Learning Reciprocity in Complex Sequential Social Dilemmas.

Tom Eccles, Edward Hughes, János Kramár, Steven Wheelwright, Joel Z. Leibo

CoRR, 2019

Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research.

Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel

CoRR, 2019

Generalization of Reinforcement Learners with Working and Episodic Memory.

Meire Fortunato, Melissa Tan, Ryan Faulkner, Steven Hansen, Adrià Puigdomènech Badia, Gavin Buttimore, Charlie Deck, Joel Z. Leibo, Charles Blundell

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Interval timing in deep reinforcement learning agents.

Ben Deverett, Ryan Faulkner, Meire Fortunato, Gregory Wayne, Joel Z. Leibo

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems.

Peter Sunehag, Guy Lever, Siqi Liu, Josh Merel, Nicolas Heess, Joel Z. Leibo, Edward Hughes, Tom Eccles, Thore Graepel

Proceedings of the 2019 Conference on Artificial Life, 2019

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning.

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

Proceedings of the 36th International Conference on Machine Learning, 2019

Evolving Intrinsic Motivations for Altruistic Behavior.

Jane X. Wang, Edward Hughes, Chrisantha Fernando, Wojciech M. Czarnecki, Edgar A. Duéñez-Guzmán, Joel Z. Leibo

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

Malthusian Reinforcement Learning.

Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

The Imitation Game: Learned Reciprocity in Markov games.

Tom Eccles, Edward Hughes, János Kramár, Steven Wheelwright, Joel Z. Leibo

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL.

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

CoRR, 2018

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.

Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio García Castañeda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, Thore Graepel

CoRR, 2018

Unsupervised Predictive Memory in a Goal-Directed Agent.

Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack W. Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Jimenez Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matthew M. Botvinick, Demis Hassabis, Timothy P. Lillicrap

CoRR, 2018

Inequity aversion resolves intertemporal social dilemmas.

Edward Hughes, Joel Z. Leibo, Matthew G. Philips, Karl Tuyls, Edgar A. Duéñez-Guzmán, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin R. McKee, Raphael Koster, Heather Roff, Thore Graepel

CoRR, 2018

Kickstarting Deep Reinforcement Learning.

Simon Schmitt, Jonathan J. Hudson, Augustin Zídek, Simon Osindero, Carl Doersch, Wojciech M. Czarnecki, Joel Z. Leibo, Heinrich Küttler, Andrew Zisserman, Karen Simonyan, S. M. Ali Eslami

CoRR, 2018

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents.

Joel Z. Leibo, Cyprien de Masson d'Autume, Daniel Zoran, David Amos, Charles Beattie, Keith Anderson, Antonio García Castañeda, Manuel Sanchez, Simon Green, Audrunas Gruslys, Shane Legg, Demis Hassabis, Matthew M. Botvinick

CoRR, 2018

Inequity aversion improves cooperation in intertemporal social dilemmas.

Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar A. Duéñez-Guzmán, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin R. McKee, Raphael Koster, Heather Roff, Thore Graepel

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Emergent Communication through Negotiation.

Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z. Leibo, Karl Tuyls, Stephen Clark

Proceedings of the 6th International Conference on Learning Representations, 2018

A Generalised Method for Empirical Game Theoretic Analysis.

Karl Tuyls, Julien Pérolat, Marc Lanctot, Joel Z. Leibo, Thore Graepel

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward.

Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

Deep Q-learning From Demonstrations.

Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017.

Matthew M. Botvinick, David G. T. Barrett, Peter W. Battaglia, Nando de Freitas, Dharshan Kumaran, Joel Z. Leibo, Tim Lillicrap, Joseph Modayil, S. Mohamed, Neil C. Rabinowitz, Danilo Jimenez Rezende, Adam Santoro, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis

CoRR, 2017

Symmetric Decomposition of Asymmetric Games.

Karl Tuyls, Julien Pérolat, Marc Lanctot, Georg Ostrovski, Rahul Savani, Joel Z. Leibo, Toby Ord, Thore Graepel, Shane Legg

CoRR, 2017

Value-Decomposition Networks For Cooperative Multi-Agent Learning.

Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel

CoRR, 2017

Learning from Demonstrations for Real World Reinforcement Learning.

Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

CoRR, 2017

A multi-agent reinforcement learning model of common-pool resource appropriation.

Julien Pérolat, Joel Z. Leibo, Vinícius Flores Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Reinforcement Learning with Unsupervised Auxiliary Tasks.

Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu

Proceedings of the 5th International Conference on Learning Representations, 2017

Learning to reinforcement learn.

Jane Wang, Zeb Kurth-Nelson, Hubert Soyer, Joel Z. Leibo, Dhruva Tirumala, Rémi Munos, Charles Blundell, Dharshan Kumaran, Matt M. Botvinick

Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

Multi-agent Reinforcement Learning in Sequential Social Dilemmas.

Joel Z. Leibo, Vinícius Flores Zambaldi, Marc Lanctot, Janusz Marecki, Thore Graepel

Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

2016

Unsupervised learning of invariant representations.

Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio

Theor. Comput. Sci., 2016

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation.

Joel Z. Leibo, Qianli Liao, Winrich Freiwald, Fabio Anselmi, Tomaso A. Poggio

CoRR, 2016

Model-Free Episodic Control.

Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z. Leibo, Jack W. Rae, Daan Wierstra, Demis Hassabis

CoRR, 2016

DeepMind Lab.

Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, Stig Petersen

CoRR, 2016

Using Fast Weights to Attend to the Recent Past.

Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

How Important Is Weight Symmetry in Backpropagation?

Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex.

Joel Z. Leibo, Qianli Liao, Fabio Anselmi, Tomaso A. Poggio

PLoS Comput. Biol., 2015

Approximate Hubel-Wiesel Modules and the Data Structures of Neural Computation.

Joel Z. Leibo, Julien Cornebise, Sergio Gómez, Demis Hassabis

CoRR, 2015

2014

Unsupervised learning of clutter-resistant visual representations from natural videos.

Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio

CoRR, 2014

Subtasks of Unconstrained Face Recognition.

Joel Z. Leibo, Qianli Liao, Tomaso A. Poggio

Proceedings of the VISAPP 2014, 2014

2013

Throwing Down the Visual Intelligence Gauntlet.

Cheston Tan, Joel Z. Leibo, Tomaso A. Poggio

Proceedings of the Machine Learning for Computer Vision, 2013

Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?

Qianli Liao, Joel Z. Leibo, Youssef Mroueh, Tomaso A. Poggio

CoRR, 2013

Unsupervised Learning of Invariant Representations in Hierarchical Architectures.

Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio

CoRR, 2013

Learning invariant representations and applications to face verification.

Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Learning and disrupting invariance in visual recognition with a temporal association rule.

Leyla Isik, Joel Z. Leibo, Tomaso A. Poggio

Frontiers Comput. Neurosci., 2012

2011

Why The Brain Separates Face Recognition From Object Recognition.

Joel Z. Leibo, Jim Mutch, Tomaso A. Poggio

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011