Reinforcement Learning

Catalog description

At the most basic level an artificial intelligence (AI) is an automated decision-making system. In order to maximize the potential of AI, we need methods to teach it to make ‘good’ decisions over long periods of time. Reinforcement learning (RL) is one such method. This course focuses on building the expertise needed to construct and train AIs using RL. We will learn about key concepts, mathematics, algorithms, and applications of RL. We will begin by introducing different types of RL problems and the algorithms available to solve them. Next we will introduce Markov decision processes which will give the agents we seek to develop a formal framework that they can use to solve problems. Following this, students will review and implement multiple classical and modern foundational RL algorithms to solve applied AI control problems. Environmental simulators, performance metrics, on/off policy algorithms, the exploration vs exploitation tradeoff, and state/action/reward engineering will be discussed. Lecture content will be reinforced with hands-on tutorial-oriented laboratory exercises for development of representative applications. Students will finish the course by designing, implementing, testing, and documenting an AI trained using RL on a problem of their choice.