Optimal Control and Dynamic Programming
MECH 6326
Erik Jonsson School of Engineering and Computer Science
Introduction to stochastic control, with applications taken from a variety of areas, including automatic control, engineering, supply-chain management, resource allocations, finance, etc. Markov chains and Markov decision processes, optimal policies and value functions with full state information for finite-horizon, infinite-horizon discounted, and average stage cost problems. Dynamic programming and Bellman equation, value iteration, policy iteration. Linear quadratic stochastic control. Approximate dynamic programming and model predictive control. 3 credit hours.
Prerequisite: MECH 6300 or instructor consent required.
Offering Frequency: Based on student interest and instructor availability
Grades: 64
Median GPA: A-
Mean GPA: 3.122