Reinforcement Learning: Leveraging Deep Learning for Control

Buhr, Craig

Title:

Reinforcement Learning: Leveraging Deep Learning for Control

dc.contributor.author	Buhr, Craig
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Robotics and Intelligent Machines	en_US
dc.contributor.corporatename	MathWorks	en_US
dc.date.accessioned	2020-11-11T21:38:50Z
dc.date.available	2020-11-11T21:38:50Z
dc.date.issued	2020-11-04
dc.description	Presented online November 4, 2020, 12:15 p.m.-1:15 p.m.	en_US
dc.description	IRIM Seminar Series: Session 5	en_US
dc.description	Craig Buhr received his B.S., M.S. and Ph.D. from the School of Mechanical Engineering at Purdue University. He joined MathWorks in 2003 as a Senior Software Engineer for the Controls and Identification products. He currently manages the engineering team responsible for the control design products (Control System Toolbox, Robust Control Toolbox, Model Predictive Control Toolbox, Simulink Control Design and Reinforcement Learning Toolbox). The team is focused on designing and developing software tools that enable engineers to efficiently design and analyse control systems for industrial applications. His research interests include dynamic system modelling, control theory, machine learning reinforcement learning and computer aided control system design.	en_US
dc.description	Runtime: 56:38 minutes	en_US
dc.description.abstract	Reinforcement learning is getting a lot of attention lately. People are excited about its potential to solve complex problems in areas such as robotics and automated driving, where traditional control methods can be challenging to use. In addition to deep neural nets to represent the policy, reinforcement learning lends itself to control problems because its training incorporates repeated exploration of the environment. As such exploration is time-consuming and costly or dangerous when done with actual hardware, a simulation model is often used to represent the environment. In this talk, we provide an overview of reinforcement learning and its application to teaching a robot to walk. We discuss the differences between reinforcement learning and traditional control methods. Specific topics of reinforcement learning covered in this presentation include: • Creating environment models • Crafting effective reward functions • Deploying to embedded devices through automatic code generation for CPUs and GPUs	en_US
dc.format.extent	56:38 minutes
dc.identifier.uri	http://hdl.handle.net/1853/63908
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	IRIM Seminar Series
dc.subject	Reinforcement learning	en_US
dc.subject	Robotics	en_US
dc.title	Reinforcement Learning: Leveraging Deep Learning for Control	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries	IRIM Seminar Series
relation.isOrgUnitOfPublication	66259949-abfd-45c2-9dcc-5a6f2c013bcf
relation.isSeriesOfPublication	9bcc24f0-cb07-4df8-9acb-94b7b80c1e46