Using Reinforcement Learning to Introduce Artificial Intelligence in the Cs Curriculum

There are many interesting topics in artificial intelligence that would be useful to stimulate student interest at various levels of the computer science curriculum. They can also be used to illustrate some basic concepts of computer science, such as arrays. One such topic is reinforcement learning – teaching a computer program how to play a game or traverse an environment using a system of rewards and punishments. There are reinforcement learning projects that can be used at all levels of the computer science curriculum. This paper describes a few examples of reinforcement learning and how they might be used. INTRODUCTION TO REINFORCEMENT LEARNING: Reinforcement learning is a topic from artificial intelligence, specifically machine learning. It functions by allowing the computer program (or agent) to learn correct choices by repeatedly making choices and observing the results. For example, a computer game would learn to play a good game of chess by playing many games of chess, and favoring moves that in the past had led to a win, while avoiding moves that have led to a loss. There are many ways to implement a reinforcement learning program, but most work by keeping a value (usually called a utility value or Q value) associated with each state and/or move option, increasing its value when it leads to a win (or a reward) and decreasing its value when it leads to a loss (or a penalty). Reinforcement learning is typically taught in artificial intelligence courses, but it can be used in other courses in the computer science curriculum – either to stimulate student interest or to spotlight a specific topic in the course or both. A simple version of reinforcement learning is