Leibo, Joel Z.

Deep Q-learning From Demonstrations

Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Osband, I., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 889

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

Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castañeda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., et al. (2019) · Science

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 662

Prefrontal cortex as a meta-reinforcement learning system

Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018) · Nature Neuroscience

Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology Studies

Cited by 596

Learning to reinforcement learn

Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., & Botvinick, M. (2016) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 579

Multi-Agent Risks from Advanced AI

Hammond, L., Chan, A., Clifton, J., Hoelscher-Obermaier, J., Khan, A., McLean, E., Smith, C., Barfuss, W., Foerster, J., Gavenčiak, T., et al. (2025) · arXiv

Ethics and Social Impacts of AIMulti-Agent Systems and Negotiation

Cited by 305

Model-Free Episodic Control

Blundell, C., Uria, B., Pritzel, A., Li, Y., Ruderman, A., Leibo, J. Z., Rae, J., Wierstra, D., & Hassabis, D. (2016) · arXiv

Reinforcement Learning in RoboticsMemory and Neural Mechanisms

Cited by 162

Inequity aversion improves cooperation in intertemporal social dilemmas

Hughes, E., Leibo, J. Z., Phillips, M. G., Tuyls, K., Duéñez-Guzmán, E. A., Castañeda, A. G., Dunning, I., Zhu, T., McKee, K. R., Koster, R., et al. (2018) · arXiv

Evolutionary Game Theory and CooperationExperimental Behavioral Economics Studies

Cited by 134

Machine culture

Brinkmann, L., Baumann, F., Bonnefon, J.-F., Derex, M., Müller, T. F., Nussberger, A.-M., Czaplicka, A., Acerbi, A., Griffiths, T. L., Henrich, J., et al. (2023) · Nature Human Behaviour

Language and cultural evolutionCybernetics and Technology in Society

Cited by 132

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

Perolat, J., Leibo, J. Z., Zambaldi, V., Beattie, C., Tuyls, K., & Graepel, T. (2017) · arXiv

Experimental Behavioral Economics StudiesEvolutionary Game Theory and Cooperation

Cited by 109

Rethink reporting of evaluation results in AI

Burnell, R., Schellaert, W., Burden, J., Ullman, T. D., Martinez-Plumed, F., Tenenbaum, J. B., Rutar, D., Cheke, L. G., Sohl-Dickstein, J., Mitchell, M., et al. (2023) · Science

Ethics and Social Impacts of AIScientific Computing and Data Management

Cited by 104

Open Problems in Cooperative AI

Dafoe, A., Hughes, E., Bachrach, Y., Collins, T., McKee, K. R., Leibo, J. Z., Larson, K., & Graepel, T. (2020) · arXiv

Multi-Agent Systems and NegotiationGame Theory and Voting Systems

Cited by 93

Unsupervised learning of invariant representations

Anselmi, F., Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., & Poggio, T. (2016) · Theoretical Computer Science

Domain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

Cited by 88

DeepMind Lab

Beattie, C., Leibo, J. Z., Teplyashin, D., Ward, T., Wainwright, M., Küttler, H., Lefrancq, A., Green, S., Valdés, V., Sadik, A., et al. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 68

Kickstarting Deep Reinforcement Learning

Schmitt, S., Hudson, J. J., Zidek, A., Osindero, S., Doersch, C., Czarnecki, W. M., Leibo, J. Z., Kuttler, H., Zisserman, A., Simonyan, K., et al. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 67

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

Leibo, J. Z., Hughes, E., Lanctot, M., & Graepel, T. (2019) · arXiv

Complex Systems and DynamicsMulti-Agent Systems and Negotiation

Cited by 60

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

Isik, L., Leibo, J. Z., & Poggio, T. (2012) · Frontiers in Computational Neuroscience

Visual perception and processing mechanismsFace Recognition and Perception

Cited by 38

Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents

Köster, R., Hadfield-Menell, D., Everett, R., Weidinger, L., Hadfield, G. K., & Leibo, J. Z. (2022) · Proceedings of the National Academy of Sciences

Reinforcement Learning in RoboticsEvolutionary Game Theory and Cooperation

Cited by 37

Multi-agent Reinforcement Learning in Sequential Social Dilemmas

Leibo, J. Z., Zambaldi, V., Lanctot, M., Marecki, J., & Graepel, T. (2017) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsEvolutionary Game Theory and Cooperation

Cited by 35

Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning

McKee, K. R., Gemp, I., McWilliams, B., Duèñez-Guzmán, E. A., Hughes, E., & Leibo, J. Z. (2020) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsExperimental Behavioral Economics Studies

Cited by 33

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Leibo, J. Z., Duéñez-Guzmán, E., Vezhnevets, A. S., Agapiou, J. P., Sunehag, P., Koster, R., Matyas, J., Beattie, C., Mordatch, I., & Graepel, T. (2021) · arXiv

Reinforcement Learning in RoboticsMulti-Agent Systems and Negotiation

Cited by 31

Learning Reciprocity in Complex Sequential Social Dilemmas

Eccles, T., Hughes, E., Kramár, J., Wheelwright, S., & Leibo, J. Z. (2019) · arXiv

Evolutionary Game Theory and CooperationExperimental Behavioral Economics Studies

Cited by 19

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

Vezhnevets, A. S., Agapiou, J. P., Aharon, A., Ziv, R., Matyas, J., Duéñez-Guzmán, E. A., Cunningham, W. A., Osindero, S., Karmon, D., & Leibo, J. Z. (2023) · arXiv

Multi-Agent Systems and NegotiationSimulation Techniques and Applications

Cited by 15

Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning

Cook, J., Lu, C., Hughes, E., Leibo, J. Z., & Foerster, J. (2024) · arXiv

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 6

A multi-agent reinforcement learning model of reputation and cooperation in human groups

McKee, K. R., Hughes, E., Zhu, T. O., Chadwick, M. J., Koster, R., Castaneda, A. G., Beattie, C., Graepel, T., Botvinick, M., & Leibo, J. Z. (2021) · arXiv

Evolutionary Game Theory and CooperationExperimental Behavioral Economics Studies

Cited by 5