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An empirical investigation of the challenges of real-world reinforcement learning


Mar 24, 2020
Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester

* arXiv admin note: text overlap with arXiv:1904.12901 

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Robust Reinforcement Learning for Continuous Control with Model Misspecification


Jun 18, 2019
Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller


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Challenges of Real-World Reinforcement Learning


Apr 29, 2019
Gabriel Dulac-Arnold, Daniel Mankowitz, Todd Hester


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Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards


Oct 08, 2018
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, Martin Riedmiller


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A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning


Oct 08, 2018
Mel Vecerik, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, Jon Scholz


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Observe and Look Further: Achieving Consistent Performance on Atari


May 29, 2018
Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, Olivier Pietquin


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Safe Exploration in Continuous Action Spaces


Jan 26, 2018
Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik, Todd Hester, Cosmin Paduraru, Yuval Tassa


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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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Adaptive Lambda Least-Squares Temporal Difference Learning


Dec 30, 2016
Timothy A. Mann, Hugo Penedones, Shie Mannor, Todd Hester


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A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control


May 21, 2011
Todd Hester, Michael Quinlan, Peter Stone

* Added a reference Presents a real-time parallel architecture for model-based reinforcement learning methods 

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