Dynamic Programming: Optimal Control

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Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.

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

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

Explores Dynamic Programming for optimal control, covering machine replacement, Markov chains, control policies, and linear quadratic problems.

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

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

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

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

Explores accelerating the Value Iteration algorithm using control theory and matrix splitting techniques to achieve faster convergence.

Explores the Asset Selling Problem to maximize long-term reward without a deadline.