## Lectures

Lecture 1: Introduction.

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Lecture 2: Examples of decision problems.

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Lecture 3: Bellman's optimality principle.

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Lecture 4: Value and Policy iteration.

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Lecture 5: Stochastic approximation.

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Lecture 6: Convergence of stochastic approximation.

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Lecture 7: RL algorithms.

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Lecture 8: Bayesian experimental design

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Lecture 9: Experimental design with Gaussian Processes

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Lecture 10: Partially observable MDPs (POMDPS)

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Lecture 11: Exact methods for POMDPS

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Lecture 12: Point based value iteration

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Lecture 13: Direct policy search

Lecture 13: Representation