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Optimal Control

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AStanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 8: Nonlinearity Aug 12, 2026 · 1:14:05 LQR is generalized from state regulation to nonlinear tracking by defining deviation variables ($\delta x, \delta u$). This allows the problem to be reformulated as an auxiliary LQR problem.

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 17: RL Value-Based Methods Aug 13, 2026 · 1:17:40 Dive into model-free Reinforcement Learning! We explore value-based methods like SARSA and Q-learning, contrasting on-policy vs. off-policy approaches. Learn how Deep Q Networks (DQN) stabilize training using Experience Replay and fixed target networks.

Stanford Online

Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 17: RL Value-Based Methods

Dive into model-free Reinforcement Learning! We explore value-based methods like SARSA and Q-learning, contrasting on-policy vs. off-policy approaches. Learn how Deep Q Networks (DQN) stabilize training using Experience Replay and fixed target networks.

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Stanford Online

AStanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 8: Nonlinearity

LQR is generalized from state regulation to nonlinear tracking by defining deviation variables ($\delta x, \delta u$). This allows the problem to be reformulated as an auxiliary LQR problem.

Read article