Lecture slides from a current course (2020) on Topics in Reinforcement Learning at Arizona State University:
Slides-Lecture 1, Slides-Lecture 2, Slides-Lecture 3, Slides-Lecture 4, Slides-Lecture 5.
This research monograph in currently progress will be available from the publishing company Athena Scientific sometime in 2020.
The purpose of the monograph is to consider the methods of rollout and approximate policy iteration, and to develop them in greater depth than in the author's recently published textbook on Reinforcement Learning (Athena Scientific, 2019).
A special focus of the monograph is rollout algorithms for both discrete deterministic and stochastic DP problems, and the development of distributed implementations in both multiagent and multiprocessor settings, aiming to take advantage of parallelism in computation.
Click here for preface and table of contents.
Chapter 1: Exact Dynamic Programming
Chapter 2: Rollout and Policy Improvement
Chapter 3: Learning Values and Policies
Chapter 4: Approximate Policy Iteration for Infinite Horizon Problem
The following papers and reports have a strong connection to material in the book, and amplify on its analysis and its range of applications.
Visits since February 15, 2020