Dynamic Programming: Optimal Control

Related lectures (33)

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

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.

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.

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

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

Explores system identification, stability criteria, and challenges in stabilizing cameras on moving platforms.

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

Covers the principles of feedback control systems and their applications in various fields.

Explores state-space representation, controllability, observability, and regulator calculation using the Ackermann method.

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

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

Covers recursion, dynamic programming, and algorithm design using divide and conquer strategies.

Explores the design and optimization of car suspensions for improved performance.

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.

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

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

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

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