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Thu Dec 08 11:00 PM -- 09:30 AM (PST) @ Area 1
Deep Reinforcement Learning
David Silver · Satinder Singh · Pieter Abbeel · Peter Chen

Workshop Home Page

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.

Rich Sutton (Invited Speaker)
Contributed Talks - Session 1 (Contributed Talks)
John Schulman (Invited Speaker)
Raia Hadsell (Invited Speaker)
Contributed Talks - Session 2 (Contributed Talks)
Chelsea Finn (Invited Speaker)
Lunch (Break)
Nando De Freitas (Invited Speaker)
Contributed Talks - Session 3 (Contributed Talks)
Posters - Session 1 (Posters)
Coffee Break (Break)
Late Breaking Talk (Talk)
Junhyuk Oh (Invited Speaker)
Josh Tenenbaum (Invited Speaker)
Panel Discussion (Discussion Panel)
Posters - Session 2 (Posters)