AERE504X Reinforcement Learning and AutonomyWelcomeThis course introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomy and decision-making systems. Introduction to probabilistic models and decision theory with focus on applied reinforcement learning. Computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Applications include air traffic control, aviation surveillance systems, autonomous vehicles, and robotics. Prerequisites: basic probability and fluency in a high-level programming language. Acknowledgement: This course is established at Iowa State with lecture notes and class materials kindly shared by Prof. Mykel Kochenderfer from Stanford University. This website contains basic information about the AERE504 class, including course info, the syllabus, contact info, and an introduction to Julia. CanvasCanvas is used for the distribution and collection of lecture notes, exams and solutions, project assignments, etc. Students must register for AERE504 to access this information at Canvas. Instructor contactProfessor Peng Wei |