Decision Making in Dynamic Learning Environments

1 Unit · Graduate

Decision making in stochastic and dynamic environments plays an essential role in many areas, including finance, robotics, game theory, revenue management and social networks. This course aims to gain a theoretical understanding for some popular tools used to solve these problems. Topics include: theoretic foundations of reinforcement learning/dynamic programming, multi-armed bandit problems and its solutions, random graph models for social and economic networks which can be used to explain small world phenomena, wealth concentration and disease/information spreading.