Temple University 3 credits MSOM
In this course, students will learn how to formulate a sequential decision making problem as a dynamic program. They will learn the classical methods to solve finite-horizon and infinite-horizon dynamic optimization problems. For many dynamic optimization problems whose optimal solutions cannot be easily obtained or practically implemented, we will introduce some useful heuristic approaches, such as value function approximation and model predictive control. For cases when decision makers have incomplete knowledge of the system, students will learn some powerful joint learning and optimizing heuristics, such as upper confidence bound algorithm. We will cover several applications of dynamic programming throughout this course, such as revenue management and pricing, and inventory control. This course aims at preparing students to do research in dynamic programming and its related areas, such as reinforcement learning.
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