Reinforcement Learning for Engineering

The key focus of this subject is the design and implementation of decision-making policies for enabling a dynamical system to behave autonomously and achieve a desired objective. This subject covers both model-based and model-free learning methods, with a focus on evaluating, contrasting, and combining methods. The influence of noisy sensor data on performance, and the trade-offs between exploration and exploitation during a learning phase, will also be covered. The examples used in this subject range across existing and emerging decision-making methods, and their application to consumer and industrial engineering systems.