Dynamic Programming: Optimal Control

Related lectures (32)

Page 3 of 4

Discusses asset selling problems and optimal selling policies based on market conditions and termination states.

Explores Networked Control Systems, addressing packet dropouts, network delays, stability, and control laws for system boundability.

Covers infinite-horizon problems in Applied Probability and Stochastic Processes.

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Explores the optimal betting strategy in a dynamic programming gambling problem, emphasizing risk preference impact.

Discusses Markov Decision Processes and dynamic programming techniques for solving optimal policies in various scenarios.

Covers Nonlinear Model Predictive Control (NMPC) principles, including setpoint stabilization and Pontryagin's Maximum Principle.

Explores multivariable control, optimal laws, and system stability theorems, emphasizing the importance of observability.

Explores the dynamic model for panel data analysis and the Markov assumption.

Presents methods for problem-solving, emphasizing 'Divide and Conquer', recursion, and dynamic programming.