Saturday Monring Videos: Deep Reinforcement Learning Workshop @NIPS 2015 (and slides)

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

• 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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