Although the theory of reinforcement learning addresses an extremely general class of learning problems with a common mathematical formulation, its power has been limited by the need to develop task-specific feature representations. A paradigm shift is occurring as researchers figure out how to use deep neural networks as function approximators in reinforcement learning algorithms; this line of work has yielded remarkable empirical results in recent years. This workshop will bring together researchers working at the intersection of deep learning and reinforcement learning, and it will help researchers with expertise in one of these fields to learn about the other.
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Thu Dec 07 02:00 PM -- 09:30 PM (PST) @ Hall A
Deep Reinforcement Learning
[ Mastering Games] [ Soft Actor-Critic] [ Active Neural Localization] [ NLP Search] [ Reproducibility] [ Backprop thru Void] [ Parameter Space Noise] [ Time-Contrastive] [ Neural Map] [ Variance Reduction] [ Sample-efficient Policy Optimization] [ One-Shot Visual Imitation] [ Deep Exploration] [ StarCraft II] [ Neural Network Dynamics] [ Overcoming Exploration in RL]