J
Joel Z. Leibo
Researcher at Google
Publications - 120
Citations - 8852
Joel Z. Leibo is an academic researcher from Google. The author has contributed to research in topics: Reinforcement learning & Cognitive neuroscience of visual object recognition. The author has an hindex of 38, co-authored 108 publications receiving 6927 citations. Previous affiliations of Joel Z. Leibo include Massachusetts Institute of Technology & McGovern Institute for Brain Research.
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Reinforcement Learning with Unsupervised Auxiliary Tasks
Max Jaderberg,Volodymyr Mnih,Wojciech Marian Czarnecki,Tom Schaul,Joel Z. Leibo,David Silver,Koray Kavukcuoglu +6 more
TL;DR: This paper significantly outperforms the previous state-of-the-art on Atari, averaging 880\% expert human performance, and a challenging suite of first-person, three-dimensional \emph{Labyrinth} tasks leading to a mean speedup in learning of 10$\times$ and averaging 87\% Expert human performance on Labyrinth.
Proceedings Article
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward
Peter Sunehag,Guy Lever,Audrunas Gruslys,Wojciech Marian Czarnecki,Vinicius Zambaldi,Max Jaderberg,Marc Lanctot,Nicolas Sonnerat,Joel Z. Leibo,Karl Tuyls,Thore Graepel +10 more
TL;DR: This work addresses the problem of cooperative multi-agent reinforcement learning with a single joint reward signal by training individual agents with a novel value decomposition network architecture, which learns to decompose the team value function into agent-wise value functions.
Posted Content
Learning to reinforcement learn
Jane X. Wang,Zeb Kurth-Nelson,Dhruva Tirumala,Hubert Soyer,Joel Z. Leibo,Rémi Munos,Charles Blundell,Dharshan Kumaran,Matthew Botvinick +8 more
TL;DR: Deep Meta-Reinforcement Learning (DML) as discussed by the authors is a meta-learning approach for reinforcement learning, where the learned RL algorithm can differ from the original one in arbitrary ways and is configured to exploit structure in the training domain.
Posted Content
Deep Q-learning from Demonstrations
Todd Hester,Matej Vecerík,Olivier Pietquin,Marc Lanctot,Tom Schaul,Bilal Piot,Dan Horgan,John Quan,Andrew Sendonaris,Gabriel Dulac-Arnold,Ian Osband,John P. Agapiou,Joel Z. Leibo,Audrunas Gruslys +13 more
TL;DR: Deep Q-learning from Demonstrations (DQfD) as mentioned in this paper leverages small sets of demonstration data to massively accelerate the learning process, and is able to automatically assess the necessary ratio of demonstrating data while learning thanks to a prioritized replay mechanism.
Journal ArticleDOI
Prefrontal cortex as a meta-reinforcement learning system
Jane X. Wang,Zeb Kurth-Nelson,Dharshan Kumaran,Dhruva Tirumala,Hubert Soyer,Joel Z. Leibo,Demis Hassabis,Matthew Botvinick +7 more
TL;DR: A new theory is presented showing how learning to learn may arise from interactions between prefrontal cortex and the dopamine system, providing a fresh foundation for future research.