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SkillsCast

Knowledge Transfer in Reinforcement Learning

23rd May 2019 in London at CodeNode

There are 1 other SkillsCast available from Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning

How can reinforcement learning algorithms benefit from knowledge learned from previous tasks? In this talk, we will dive into recent works in transfer learning, multi-task learning and meta-learning. We will look at several transfer approaches and compare their differences. After the talk, you will understand the current state-of-the-art of knowledge transfer in reinforcement learning and their applications.

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Knowledge Transfer in Reinforcement Learning

Jin Cong Ho

Jin Cong Ho is a final year computer science student at the University of Nottingham, specializing in machine learning. He recently completed his undergraduate dissertation on multi-task reinforcement learning. His primary interest is to deploy the latest research advances in the larger production environment to create value.

SkillsCast

How can reinforcement learning algorithms benefit from knowledge learned from previous tasks? In this talk, we will dive into recent works in transfer learning, multi-task learning and meta-learning. We will look at several transfer approaches and compare their differences. After the talk, you will understand the current state-of-the-art of knowledge transfer in reinforcement learning and their applications.

YOU MAY ALSO LIKE:

Thanks to our sponsors

About the Speaker

Knowledge Transfer in Reinforcement Learning

Jin Cong Ho

Jin Cong Ho is a final year computer science student at the University of Nottingham, specializing in machine learning. He recently completed his undergraduate dissertation on multi-task reinforcement learning. His primary interest is to deploy the latest research advances in the larger production environment to create value.