Reinforcement Learning: Leveraging Deep Learning for Controls


Reinforcement learning allows you to solve control problems using deep learning but without using labeled data. Instead, learning occurs through multiple simulations of the system of interest. This simulation data is used to train a policy represented by a deep neural network that would then replace a traditional controller or decision-making system.

In this session, you will learn how to do reinforcement learning using MathWorks products, including how to set up environment models, define the policy structure and scale training through parallel computing to improve performance.

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand. 

About the Presenter

Emmanouil Tzorakoleftherakis is a product manager at MathWorks, with a focus on reinforcement learning and control systems. Prior to this, Emmanouil has been a research intern at Siemens Corporate Technology, and a research assistant at Northwestern University. Emmanouil has a M.S. and a Ph.D. in Mechanical Engineering from Northwestern University, and a B.S. in Electrical and Computer Engineering from University of Patras in Greece.

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