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