Here are four videos (and paprts) from the Deep Reinforcement workshop at NIPS

Contributed Papers

- Honglak Lee,
**video; Deep Reinforcement Learning with Predictions** - Juergen Schmidhuber, Reinforcement Learning of Programs in General Purpose Computers with Memory
- Michael Bowling
- Volodymyr Mnih,
**video: Faster Deep Reinforcement Learning** - Gerry Tesauro, Deep RL and Games Research at IBM
- Osaro, tech talk
- Sergey Levine,
**Video: Deep Sensorimotor Learning for Robotic Control** - Yoshua Bengio
- Martin Riedmiller,
**video; Deep RL for Learning Machines** - Jan Koutnik, Compressed Neural Networks for Reinforcement Learning

Contributed Papers

*The importance of experience replay database composition in deep reinforcement learning*Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuška-
*Continuous deep-time neural reinforcement learning*Davide Zambrano, Pieter R. Roelfsema and Sander M. Bohte -
*Memory-based control with recurrent neural networks*Nicolas Heess, Jonathan J Hunt, Timothy Lillicrap, David Silver -
*How to discount deep reinforcement learning: towards new dynamic strategies*Vincent François-Lavet, Raphael Fonteneau, Damien Ernst -
*Strategic Dialogue Management via Deep Reinforcement Learning*Heriberto Cuayáhuitl, Simon Keizer, Oliver Lemon -
*Deep Reinforcement Learning in Parameterized Action Space*Matthew Hausknecht, Peter Stone -
*Guided Cost Learning: Inverse Optimal Control with Multilayer Neural Networks*Chelsea Finn, Sergey Levine, Pieter Abbeel -
*Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search*Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel -
*Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning*Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov -
*Deep Inverse Reinforcement Learning*Markus Wulfmeier, Peter Ondruska and Ingmar Posner -
*ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources*Janarthanan Rajendran, P Prasanna, Balaraman Ravindran, Mitesh Khapra -
*Q-Networks for Binary Vector Actions*Naoto Yoshida -
*The option-critic architecture*Pierre-Luc Bacon and Doina Precup -
*Learning Deep Neural Network Policies with Continuous Memory States*Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel -
*Deep Attention Recurrent Q-Network*Ivan Sorokin, Alexey Seleznev, Mikhail Pavlov, Aleksandr Fedorov, Anastasiia Ignateva -
*Generating Text with Deep Reinforcement Learning*Hongyu Guo -
*Deep Spatial Autoencoders for Visuomotor Learning*Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel -
*Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models*John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth -
*One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors*Justin Fu, Sergey Levine, Pieter Abbeel -
*Learning Visual Models of Physics for Playing Billiards*Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik -
*Conditional computation in neural networks for faster models*Emmanuel Bengio, Joelle Pineau, Pierre-Luc Bacon, Doina Precup -
*Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models*Bradly C. Stadie, Sergey Levine, Pieter Abbeel -
*Learning Simple Algorithms from Examples*Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus

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