Applied Reinforcement Learning

Catalog description

Introduces reinforcement learning, a subset of machine learning that focuses on training agents to make decisions with the aim of achieving specific objectives. Emphasizing practical experience, RL methods can be utilized to address real-world problems, including tasks related to design and optimization. At its core, RL revolves around agents interacting with their environments, learning from rewards and punishments, and continuously improving their decision-making skills. Emphasizes the adaptability of RL across various domains and how, with practical RL expertise and algorithms, RL can effectively address a wide array of industry challenges, ultimately enhancing its applicability and success in various professional settings.