This is a paper aimed at a transportation audience that provides a review of the universal framework, and then focuses on two forms of direct lookahead approximations (DLA) that tend to be useful in transportation and logistics: stochastic lookaheads, and parameterized deterministic lookaheads.
#Tinylink series
Notes on notation for sequential decision problems:Ī discussion of the issue of tunable parameters that arise in any form of policy search:Ī quick introduction to the four classes of policies that I compiled from a series of posts on LinkedIn:
The webpage “On state variables” discusses this topic: One of the most confused topics in reinforcement learning is state variables (try to find a definition of a state variable in any MDP or RL book). Original link: Short notes about sequential decision analytics The course evolves from basic machine learning, through simple sequential decision problems using parametric policies, to the familiar topics (in OR) of linear, integer and nonlinear programming, where each of these are presented initially as static problems, and then as sequential problems. I have prepared a new way of teaching introductory optimization for undergraduates and graduates. Below is a link to a spreadsheet where students can tune the parameters of a “buy low, sell high” policy for energy storage: There is an online version of the 2nd edition of my book Optimal Learning along with all the lectures at:Ī great way to teach about optimizing over policies is to tune the parameters of a simple PFA policy. Optimal learning problems are pure learning problems, but arise in a wide range of settings.
I taught a course called Optimal Learning to undergraduates at Princeton. If you are thinking of teaching this material, please add your name to the signup list at: These course descriptions can be found at:
#Tinylink free
This is a companion book for RLSO written for an undergraduate level course (this is a free download, and has a python module for most of the chapters): The link to the page for Sequential Decision Analytics and Modeling. The link to the page for Reinforcement Learning and Stochastic Optimization: This provides a more in-depth introduction to the modeling framework and the four classes of policies:
My best video introduction to sequential decision analytics was prepared for a Distinguished Speaker series in supply chain management:Ī four part tutorial that I have given several times, most recently to a class at Oxford in December, 2022. New! I prepared a webpage with a direct comparison of classical “reinforcement learning” (as captured by Sutton and Barto) and my RLSO book: There is a lot of interest in “reinforcement learning” but a surprising lack of consensus on precisely what this means.
My original “jungle of stochastic optimization” webpage:Ī discussion that bridges “reinforcement learning” to “sequential decision analytics”: Introduction to the fields of sequential decision problems:Īn introduction to the field I am calling “sequential decision analytics”: Please use the “tinyurl” link if you can so I can see which links are attracting the most interest. This page will list the most interesting resources, including both the “tinyurl” link and the original link. Also, I have been using “tinyurl” links because they are easier to remember (and I can track traffic), but not everyone can use these links. I have been accumulating a number of links to pages on “sequential decision analytics” and my new book Reinforcement Learning and Stochastic Optimization.
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