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Tutorial
Deep Reinforcement Learning, Decision Making, and Control
Sergey Levine · Chelsea Finn

Sun Aug 6th 01:00 -- 03:15 PM @ Parkside 1
in Tutorials Session B »

Deep learning methods, which combine high-capacity neural network models with simple and scalable training algorithms, have made a tremendous impact across a range of supervised learning domains, including computer vision, speech recognition, and natural language processing. This success has been enabled by the ability of deep networks to capture complex, high-dimensional functions and learn flexible distributed representations. Can this capability be brought to bear on real-world decision making and control problems, where the machine must not only classify complex sensory patterns, but choose actions and reason about their long-term consequences?

Decision making and control problems lack the close supervision present in more classic deep learning applications, and present a number of challenges that necessitate new algorithmic developments. In this tutorial, we will cover the foundational theory of reinforcement and optimal control as it relates to deep reinforcement learning, discuss a number of recent results on extending deep learning into decision making and control, including model-based algorithms, imitation learning, and inverse reinforcement learning, and explore the frontiers and limitations of current deep reinforcement learning algorithms

Author Information

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 computer vision and graphics. 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.

Chelsea Finn (Stanford, Google, UC Berkeley)
Chelsea Finn

Chelsea Finn is a research scientist at Google Brain and a post-doctoral scholar at UC Berkeley. In September 2019, she will be joining Stanford's computer science department as an assistant professor. Finn's research interests lie in the ability to enable robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, Finn has developed deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for scalable acquisition of nonlinear reward functions, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelors degree in EECS at MIT, and her PhD in CS at UC Berkeley. Her research has been recognized through an NSF graduate fellowship, a Facebook fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg.

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