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Teaching » Syllabus: Reinforcement Learning

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Date

Topic

Assignments

Aug. 28

No Class -- but Reading Assignment Chapter 1
Chapter 2

Sept. 4

The Basics of Reinforcement Learning Chapter 3

Sept. 11

Dynamic Programming Chapter 4

Sept. 18

Monte Carlo Methods Chapter 5

Sept. 25

Temporal Difference Learning Chapter 6

Oct. 2

Eligibility Traces Chapter 7

Oct. 9

Generalization and Function Approximation Chapter 8
Paper {1}

Oct. 16

Planning and Learning Chapter 9

Oct. 23

Policy Gradient Methods I Papers {1}{2}{3},
optional {4}{5}

Oct. 30

No class

Nov. 6

Policy Gradient Methods II Papers {1}

Nov. 13

Partially Observable Reinforcement Learning Problems Paper {1}

Nov. 20

Hierarchical Reinforcement Learning Paper {1}

Nov. 27

Higher Level Actions, Abstraction, Action Primitives, Options Paper {1}

Dec. 4

Project Presentations
Designed by: Nerses Ohanyan & Jan Peters
Page last modified on October 21, 2005, at 05:59 PM