Summer 2014
These videos and lecture notes are from a 6-lecture, 12-hour short course on Approximate Dynamic Programming, taught by Professor Dimitri P. Bertsekas at Tsinghua University in Beijing, China in June 2014. They focus primarily on the advanced research-oriented issues of large scale infinite horizon dynamic programming, which corresponds to lectures 11-23 of the MIT 6.231 course.
The complete set of lecture notes are available here: Complete Slides (PDF - 1.6MB), and are also divided by lecture below. Additional supporting material can be obtained on Prof. Bertsekas' web site.
Note To OCW Users: All videos are from Shuvomoy Das Gupta on Youtube and are not provided under our Creative Commons License.
TOPICS | VIDEO LECTURES | LECTURE NOTES |
---|---|---|
Introduction to Dynamic Programming (DP)
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Approximate Dynamic Programming, Lecture 1, Part 1 (00:52:25) Approximate Dynamic Programming, Lecture 1, Part 1 | Lecture 1 (PDF) |
Approximate Dynamic Programming, Lecture 1, Part 2 (00:41:56) Approximate Dynamic Programming, Lecture 1, Part 2 | ||
Approximate Dynamic Programming, Lecture 1, Part 3 (00:28:12) Approximate Dynamic Programming, Lecture 1, Part 3 | ||
Review of Discounted Problem Theory, Shorthand Notation
|
Approximate Dynamic Programming, Lecture 2, Part 1 (00:38:47) Approximate Dynamic Programming, Lecture 2, Part 1 | Lecture 2 (PDF) |
Approximate Dynamic Programming, Lecture 2, Part 2 (00:45:40) Approximate Dynamic Programming, Lecture 2, Part 2 | ||
Approximate Dynamic Programming, Lecture 2, Part 3 (00:31:00) Approximate Dynamic Programming, Lecture 2, Part 3 | ||
General Issues of Approximation and Simulation for Large-Scale Problems
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Approximate Dynamic Programming, Lecture 3, Part 1 (01:12:44) Approximate Dynamic Programming, Lecture 3, Part 1 | Lecture 3 (PDF) |
Approximate Dynamic Programming, Lecture 3, Part 2 (00:56:00) Approximate Dynamic Programming, Lecture 3, Part 2 | ||
Approximate Policy Iteration based on Temporal Differences, Projected Equations, Galerkin Approximation
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Approximate Dynamic Programming, Lecture 4, Part 1 (00:38:13) Approximate Dynamic Programming, Lecture 4, Part 1 | Lecture 4 (PDF) |
Approximate Dynamic Programming, Lecture 4, Part 2 (00:45:30) Approximate Dynamic Programming, Lecture 4, Part 2 | ||
Aggregation Methods
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Approximate Dynamic Programming, Lecture 5, Part 1 (00:38:25) Approximate Dynamic Programming, Lecture 5, Part 1 | Lecture 5 (PDF) |
Approximate Dynamic Programming, Lecture 5, Part 2 (00:36:27) Approximate Dynamic Programming, Lecture 5, Part 2 | ||
Approximate Dynamic Programming, Lecture 5, Part 3 (00:40:45) Approximate Dynamic Programming, Lecture 5, Part 3 | ||
Q-Learning, Approximation in Policy Space
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Approximate Dynamic Programming, Lecture 6, Part 1 (00:47:43) Approximate Dynamic Programming, Lecture 6, Part 1 | Lecture 6 (PDF) |
Approximate Dynamic Programming, Lecture 6, Part 2 (00:45:18) Approximate Dynamic Programming, Lecture 6, Part 2 |
Summer 2012
These notes are from a condensed, more research-oriented version of the course, given by Prof. Bertsekas in Summer 2012.