Timezone: »

Deep Learning for Action and Interaction
Chelsea Finn · Raia Hadsell · David Held · Sergey Levine · Percy Liang

Fri Dec 09 11:00 PM -- 09:30 AM (PST) @ Area 3
Event URL: https://sites.google.com/site/nips16interaction/ »

Deep learning systems that act in and interact with an environment must reason about how actions will change the world around them. The natural regime for such real-world decision problems involves supervision that is weak, delayed, or entirely absent, and the outputs are typically in the context of sequential decision processes, where each decision affects the next input. This regime poses a challenge for deep learning algorithms, which typically excel with: (1) large amounts of strongly supervised data and (2) a stationary distribution of independently observed inputs. The algorithmic tools for tackling these challenges have traditionally come from reinforcement learning, optimal control, and planning, and indeed the intersection of reinforcement learning and deep learning is currently an exciting and active research area. At the same time, deep learning methods for interactive decision-making domains have also been proposed in computer vision, robotics, and natural language processing, often using different tools and algorithmic formalisms from classical reinforcement learning, such as direct supervised learning, imitation learning, and model-based control. The aim of this workshop will be to bring together researchers across these disparate fields. The workshop program will focus on both the algorithmic and theoretical foundations of decision making and interaction with deep learning, and the practical challenges associated with bringing to bear deep learning methods in interactive settings, such as robotics, autonomous vehicles, and interactive agents.

Author Information

Chelsea Finn (UC Berkeley)
Raia Hadsell (DeepMind)
David Held (UC Berkeley)
Sergey Levine (UC Berkeley)
Sergey Levine

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as applications in other decision-making domains. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more

Percy Liang (Stanford University)
Percy Liang

Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).

More from the Same Authors