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Hypermodels for Exploration

Jun 12, 2020
Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Ian Osband, Zheng Wen, Benjamin Van Roy

* Published as a conference paper at ICLR 2020 

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Stochastic matrix games with bandit feedback

Jun 09, 2020
Brendan O'Donoghue, Tor Lattimore, Ian Osband


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Making Sense of Reinforcement Learning and Probabilistic Inference

Feb 14, 2020
Brendan O'Donoghue, Ian Osband, Catalin Ionescu

* ICLR 2020 

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Behaviour Suite for Reinforcement Learning

Aug 13, 2019
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepezvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt


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Meta-learning of Sequential Strategies

May 08, 2019
Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin Miller, Mohammad Azar, Ian Osband, Neil Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg

* DeepMind Technical Report (15 pages, 6 figures) 

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The Uncertainty Bellman Equation and Exploration

Oct 22, 2018
Brendan O'Donoghue, Ian Osband, Remi Munos, Volodymyr Mnih


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Randomized Prior Functions for Deep Reinforcement Learning

Jun 08, 2018
Ian Osband, John Aslanides, Albin Cassirer


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Deep Exploration via Randomized Value Functions

Jun 06, 2018
Ian Osband, Benjamin Van Roy, Daniel Russo, Zheng Wen


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Scalable Coordinated Exploration in Concurrent Reinforcement Learning

May 23, 2018
Maria Dimakopoulou, Ian Osband, Benjamin Van Roy


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Noisy Networks for Exploration

Feb 15, 2018
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

* ICLR 2018 

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Gaussian-Dirichlet Posterior Dominance in Sequential Learning

Feb 09, 2018
Ian Osband, Benjamin Van Roy


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Deep Q-learning from Demonstrations

Nov 22, 2017
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrunas Gruslys

* Published at AAAI 2018. Previously on arxiv as "Learning from Demonstrations for Real World Reinforcement Learning" 

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A Tutorial on Thompson Sampling

Nov 19, 2017
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen


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Minimax Regret Bounds for Reinforcement Learning

Jul 01, 2017
Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos


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On Optimistic versus Randomized Exploration in Reinforcement Learning

Jun 13, 2017
Ian Osband, Benjamin Van Roy

* Extended abstract for RLDM 2017 

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Why is Posterior Sampling Better than Optimism for Reinforcement Learning?

Jun 13, 2017
Ian Osband, Benjamin Van Roy


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On Lower Bounds for Regret in Reinforcement Learning

Aug 09, 2016
Ian Osband, Benjamin Van Roy


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Posterior Sampling for Reinforcement Learning Without Episodes

Aug 09, 2016
Ian Osband, Benjamin Van Roy


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Deep Exploration via Bootstrapped DQN

Jul 04, 2016
Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy


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Generalization and Exploration via Randomized Value Functions

Feb 15, 2016
Ian Osband, Benjamin Van Roy, Zheng Wen

* arXiv admin note: text overlap with arXiv:1307.4847 

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Bootstrapped Thompson Sampling and Deep Exploration

Jul 01, 2015
Ian Osband, Benjamin Van Roy


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Model-based Reinforcement Learning and the Eluder Dimension

Oct 31, 2014
Ian Osband, Benjamin Van Roy


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Near-optimal Reinforcement Learning in Factored MDPs

Oct 31, 2014
Ian Osband, Benjamin Van Roy


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(More) Efficient Reinforcement Learning via Posterior Sampling

Dec 26, 2013
Ian Osband, Daniel Russo, Benjamin Van Roy

* 10 pages 

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