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 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.