Congratulations once again to all the authors. Three papers were selected for a 10 minute spotlight presentation. They are:

 

Gradient Boosting for Reinforcement Learning in Complex Domains (pdf)

David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert Schapire

 

Deep Reinforcement Learning with Temporal Abstraction and Intrinsic Motivation (pdf)

Tejas Kulkarni, Karthik Narasimhan*, Ardavan Saeedi, Joshua Tenenbaum

 

Reinforcement learning of conditional computation policies for neural networks (pdf)

Emmanuel Bengio, Pierre-Luc Bacon, Ryan Lowe, Joelle Pineau, Doina Precup

 

Please see below the list of accepted papers for the Abstraction in RL Workshop:

 

Learning Purposeful Behaviour in the Absence of Rewards (pdf)

Marlos C. Machado, Michael Bowling

 

Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations (pdf)

Akshay Krishnamurthy, Alekh Agarwal, John Langford

 

Planning with Abstract Markov Decision Processes (pdf)

Nakul Gopalan , Marie desJardins, Michael L. Littman, James MacGlashan, Shawn Squire, Stefanie Tellex, John Winder , Lawson L.S. Wong

 

Simultaneous Machine Translation using Deep Reinforcement Learning (pdf)

Harsh Satija, Joelle Pineau

 

ADC: Concept Discovery and Learning of Actions for Unknown Environments (pdf)

Ana C. Tenorio-González, Eduardo F. Morales

 

Deep Conditional Multi-Task Learning in Atari (pdf)

Joshua Romoff, Emmanuel Bengio, Joelle Pineau

 

Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks (pdf)

Ramnandan Krishnamurthy, Aravind Srinivas Lakshminarayanan, Peeyush Kumar, Balaraman Ravindran

 

Probabilistic Inference for Determining Options in Reinforcement (pdf)

Christian Daniel, Herke Van Hoof, Jan Peters, Gerhard Neumann

 

Deep Reinforcement Learning in Large Discrete Action Spaces (pdf)

Gabriel Dulac-Arnold, Rich Evans, Hado van Hasselt, Peter Sunehag