The OR community has studied sequential decision problems under names such as dynamic programming, stochastic programming, simulation optimization, stochastic search, optimal control, decision trees, and multiarmed bandit problems. We often use terms such as approximate dynamic programming, neuro-dynamic programming and, increasingly, reinforcement learning.
All of these fields study sequential decision problems, using a variety of methods that are typically biased toward Bellman's equation.
I have pulled all of these topics under an umbrella that I like to call "sequential decision analytics." I introduce four classes of policies that cover *all* methods that have been suggested for making decisions. Only one of the four classes uses Bellman's equation.
For a video introduction, I suggest
https://tinyurl.com/sdafieldyoutube/
I have a webpage introduction at
https://tinyurl.com/sdafield/
Finally, I have a resources webpage at
https://tinyurl.com/sdalinks/
Where you can find links to two books, videos, webpages, and courses for different communities (undergraduate, graduate, MBAs, and a suggested weekly seminar that graduate students can organize on their own). I even suggest a way to introduce this material in a few lectures in an existing course.
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Warrren Powell
Chief analytics officer, Optimal Dynamics
Professor Emeritus, Princeton University
http://www.castlelab.princeton.edu/------------------------------