Lectures related to Dynamic Programming: Optimal Control

Linear Quadratic (LQ) Optimal Control: Proof of Theorem

Covers the proof of the recursive formula for the optimal gains in LQ control over a finite horizon.

Linear Quadratic Optimal Control: Analysis and Solution

Explores Linear Quadratic optimal control, analyzing cost, and presenting the solution to the FH-LQ problem.

Infinite-Horizon LQ Control: Solution & Example

Explores Infinite-Horizon Linear Quadratic (LQ) optimal control, emphasizing solution methods and practical examples.

Advanced Machine Learning: Discrete Reinforcement Learning

Introduces the basics of Reinforcement Learning, covering discrete states, actions, policies, value functions, MDPs, and optimal policies.

Markov Decision Processes: Foundations of Reinforcement Learning

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Distributed LQR Control Design

Introduces a suboptimal distributed LQR design for physically coupled systems with stability guarantees and simulation comparisons.

Stability: Poles, Zeros, and Control

Covers stability, poles, zeros, and control in dynamic systems, emphasizing the importance of observability.

Multivariable Control: Weight Design and Stability Analysis

Explores weight design and stability analysis in multivariable control systems, emphasizing Lyapunov theory and LQR stability.

Optimal Control: Linear Quadratic Regulation

Explores linear quadratic regulation for optimal control of linear systems, focusing on minimizing a quadratic cost function to move the system state towards zero.

Policy Iteration and Linear Programming in MDPs

Discusses policy iteration and linear programming methods for solving Markov Decision Processes.

Markov Decision Processes: Dynamic Programming Techniques

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

Multivariable Control: Stability and Lyapunov Theorems

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

Panel data: dynamic model MOOC: Selected Topics on Discrete Choice

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

Problem Solving Strategies: General Overview

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

Panel data: dynamic model with panel effects MOOC: Selected Topics on Discrete Choice

Covers the Dynamic Markov model with panel effects, addressing initial condition and endogeneity.

State Representation and Controller Design

Covers state representation, controller design, LQR controllers, and optimal control criteria.

Dynamic Programming: Optimal Control Log in to Mediaspace to watch this video

Explores Dynamic Programming for optimal control, focusing on stability, stationary policy, and recursive solutions.

Interactive Lecture: Reinforcement Learning Log in to Mediaspace to watch this video

Explores advanced reinforcement learning topics, including policies, value functions, Bellman recursion, and on-policy TD control.

Nonlinear Model Predictive Control Log in to Mediaspace to watch this video

Explores Nonlinear Model Predictive Control, covering stability, optimality, pitfalls, and examples.

Stochastic Optimal Control: Martingale Theorem Log in to Mediaspace to watch this video

Explores Stochastic Optimal Control, emphasizing Optimal Consumption and Investment, the Martingale Representation Theorem, and the Verification Theorem.