Accepted Papers

  1. Classifying Options for Deep Reinforcement Learning. Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan and Anil A. Bharath.
  2. Model-based Reinforcement Learning with Neural Networks on Hierarchical Dynamic System. Akihiko Yamaguchi and Christopher Atkeson.
  3. A Deep Hierarchical Approach to Lifelong Learning in Minecraft. Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J Mankowitz and Shie Mannor.
  4. Data-Efficient Deep Reinforcement Learning with Bayesian Neural Network Dynamics Models. Yarin Gal, Rowan McAllister and Carl Rasmussen.
  5. Deep Reinforcement Learning in a 3-D Blockworld Environment. Trevor Barron, Matthew Whitehead and Alan Yeung.
  6. Dynamic Frame skip Deep Q Network. Aravind Srinivas Lakshminarayanan, Sahil Sharma and Balaraman Ravindran.
  7. Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks. Jakob Foerster, Yannis Assael, Nando de Freitas and Shimon Whiteson.
  8. Initial Progress in Transfer for Deep Reinforcement Learning Algorithms. Yunshu Du, Gabriel de La Cruz, James Irwin and Matthew Taylor.
  9. On-Policy vs. Off-Policy Updates for Deep Reinforcement Learning. Matthew Hausknecht and Peter Stone.
  10. Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes. Caglar Gulcehre, Sarath Chandar, Kyunghyun Cho and Yoshua Bengio.
  11. Perceptual Reward Functions. Ashley Edwards, Charles Isbell and Atsuo Takanishi.