World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
9512
World Ranking
10009
National Ranking
626

Overview

Joel Z. Leibo is affiliated with DeepMind in the United Kingdom and has a significant body of research focused on social sciences, particularly in the intersection of artificial intelligence and behavioral studies. Their work spans multiple subfields including sociology and political science, artificial intelligence, safety research, management science and operations research, and cognitive neuroscience.

Leibo's research contributions are concentrated in areas such as evolutionary game theory and cooperation, experimental behavioral economics studies, reinforcement learning in robotics, language and cultural evolution, game theory and applications, evolutionary psychology and human behavior, and auction theory and applications.

Frequent co-authors collaborating with Leibo include Edgar A. Duéñez-Guzmán, Edward Hughes, Alexander Sasha Vezhnevets, Kevin R. McKee, and Raphaël Koster. These collaborations reflect a multidisciplinary approach integrating different perspectives within the social and computational sciences.

Leibo has published extensively in prominent venues, with a notable presence in arXiv (Cornell University), where 42 publications are recorded. Other publication venues include the Proceedings of the National Academy of Sciences, Behavioral and Brain Sciences, Autonomous Agents and Multi-Agent Systems, and Neuron.

Selected recent papers illustrate the breadth of their research topics and include:

  • Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver, 2023, arXiv (Cornell University)
  • Promises and challenges of human computational ethology, 2021, Neuron
  • Machine culture, 2023, Nature Human Behaviour
  • Rethink reporting of evaluation results in AI, 2023, Science
  • Negotiating team formation using deep reinforcement learning, 2020, Artificial Intelligence

Best Publications

  • Reinforcement Learning with Unsupervised Auxiliary Tasks

    Max Jaderberg;Volodymyr Mnih;Wojciech Marian Czarnecki;Tom Schaul

  • Prefrontal cortex as a meta-reinforcement learning system

    Jane X. Wang;Zeb Kurth-Nelson;Dharshan Kumaran;Dhruva Tirumala

  • Learning to reinforcement learn

    Jane X. Wang;Zeb Kurth-Nelson;Dhruva Tirumala;Hubert Soyer

  • Deep Q-learning from Demonstrations

    Todd Hester;Matej Vecerik;Olivier Pietquin;Marc Lanctot

  • Deep Q-learning From Demonstrations.

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

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

    Max Jaderberg;Wojciech M. Czarnecki;Iain Dunning;Luke Marris

  • Learning to reinforcement learn

    Jane X Wang;Zeb Kurth-Nelson;Dhruva Tirumala;Hubert Soyer

  • Value-Decomposition Networks For Cooperative Multi-Agent Learning

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

  • Multi-agent Reinforcement Learning in Sequential Social Dilemmas

    Joel Z. Leibo;Vinicius Zambaldi;Marc Lanctot;Janusz Marecki

  • Human-level performance in 3D multiplayer games with population-based reinforcement learning

    Max Jaderberg;Wojciech M. Czarnecki;Iain Dunning;Luke Marris

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

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

  • The dynamics of invariant object recognition in the human visual system.

    Leyla Isik;Ethan M. Meyers;Ethan M. Meyers;Joel Z. Leibo;Joel Z. Leibo;Tomaso A. Poggio;Tomaso A. Poggio

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

    Natasha Jaques;Angeliki Lazaridou;Edward Hughes;Çaglar Gülçehre

  • Multi-agent Reinforcement Learning in Sequential Social Dilemmas

    Joel Z. Leibo;Vinicius Zambaldi;Marc Lanctot;Janusz Marecki

  • Unsupervised Predictive Memory in a Goal-Directed Agent

    Greg Wayne;Chia-Chun Hung;David Amos;Mehdi Mirza

  • Model-Free Episodic Control

    Charles Blundell;Benigno Uria;Alexander Pritzel;Yazhe Li

  • Using Fast Weights to Attend to the Recent Past

    Jimmy Ba;Geoffrey E. Hinton;Volodymyr Mnih;Joel Z. Leibo

  • Inequity aversion improves cooperation in intertemporal social dilemmas

    Edward Hughes;Joel Z. Leibo;Matthew G. Phillips;Karl Tuyls

  • Learning from Demonstrations for Real World Reinforcement Learning

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • How important is weight symmetry in backpropagation

    Qianli Liao;Joel Z. Leibo;Tomaso Poggio

  • Unsupervised learning of invariant representations

    Fabio Anselmi;Joel Z. Leibo;Lorenzo Rosasco;Jim Mutch

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

    Julien Pérolat;Joel Z. Leibo;Vinícius Flores Zambaldi;Charles Beattie

  • Emergent Communication through Negotiation

    Kris Cao;Angeliki Lazaridou;Marc Lanctot;Joel Z. Leibo

  • Unsupervised Learning of Invariant Representations in Hierarchical Architectures

    Fabio Anselmi;Joel Z. Leibo;Lorenzo Rosasco;Jim Mutch

  • Open Problems in Cooperative AI

    Allan Dafoe;Edward Hughes;Yoram Bachrach;Tantum Collins

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