Reinforcement Learning in Autonomous Systems

Catalog description

This course will guide students through the development and operationalization of reinforcement learning frameworks. Reinforcement learning architectures such as Markov decision processes, Q-Learning, artificial neural networks, recurrent neural networks, and generative adversarial networks will be explored. This course is a guide to the complexities of reinforcement learning, their respective architectures and implementations within an autonomous system.