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Reinforcement Learning Theory

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 9: Stochastic Dyn. Program Aug 12, 2026 · 1:17:01 This lecture extends optimal control to stochastic settings using Markov Decision Processes (MDPs). The core methodology involves defining state transitions and costs that incorporate random disturbances ($w_k$).

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 9: Stochastic Dyn. Program

This lecture extends optimal control to stochastic settings using Markov Decision Processes (MDPs). The core methodology involves defining state transitions and costs that incorporate random disturbances ($w_k$).

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