Practical insights into deep reinforcement learning
Sahika Genc dives deep into the current state-of-the-art techniques in deep reinforcement learning (DRL) for a variety of use cases.
Reinforcement learning (RL) is an advanced machine learning (ML) technique that makes short-term decisions while optimizing for a longer-term goal through trial and error. DRL is a subfield of RL that uses deep learning techniques to learn from raw sensor inputs, such as pixels from an image, without feature engineering to extract explicit information such as the borders of objects in an image. The cloud has lowered the barrier to entry through low-cost, highly scalable computing power, making DRL more accessible than ever to data scientists and developers in domains such as robotics, automation, and operations research.
Sahika explores the importance of DRL and how DRL agents learn. You’ll leave understanding how DRL agents accomplish challenges such as assisting manufacturing plant operators to program and repurpose industrial robotic platforms effectively and at a lower software cost; building managers to automatically adjust building temperature, ventilation, and lighting and reduce operating costs; and ecommerce providers to personalize product recommendations and adjust for rapidly changing trends.
What you'll learn
- Understand the importance of DRL and how DRL agents learn
Sahika Genc is a senior applied scientist at Amazon artificial intelligence (AI). Her research interests are in smart automation, robotics, predictive control and optimization, and reinforcement learning (RL), and she serves in the industrial committee for the International Federation of Automatic Control. She leads science teams in scalable autonomous driving and automation systems, including consumer products such as AWS DeepRacer and SageMaker RL. Previously, she was a senior research scientist in the Artificial Intelligence and Learning Laboratory at the General Electric (GE) Global Research Center, where she led science teams on healthcare analytics and collaborated with government organizations and research institutions to develop energy analytics for consumers and utilities, served in the organizing committees for the American Control Conference, and was an associate editor for IEEE Transactions on Automation Science and Engineering. She has more than 30 patents and 50 conference, journal, and technical report publications. She earned her MS and PhD degrees in electrical engineering systems from the University of Michigan-Ann Arbor.
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