Reinforcement Learning and Neural Networks in Intelligent Control

Catalog description

Introduction to reinforcement learning and neural networks for intelligent control. Inspired by neuroscience and bio-inspired machine learning, combines deep neural networks with a reinforcement learning architecture that enables intelligent agents to learn from their actions similar to the way humans learn from experience. Emphasis on conceptual understanding of deep reinforcement learning algorithms and their applications to practical design or intelligent control systems. Prerequisite(s): Undergraduate level MTH 2350 Minimum Grade of D Corequisite(s): EE4190L