ROLLOUT, POLICY ITERATION, AND DISTRIBUTED REINFORCEMENT LEARNING

BOOK AND COURSE MATERIAL, 2020

Dimitri P. Bertsekas


CURRENT REINFORCEMENT LEARNING COURSE AT ASU, 2020: SLIDES

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.


ROLLOUT, POLICY ITERATION, AND DISTRIBUTED REINFORCEMENT LEARNING BOOK, Athena Scientific, 2020

RLCOVER.jpg

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.


BOOK PREFACE, CONTENTS, SELECTED SECTIONS, AND RELATED MATERIAL

Click here for preface and table of contents.

Book chapters:

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.

  • Bertsekas, D., "Multiagent Rollout Algorithms and Reinforcement Learning," arXiv preprint arXiv:1910.00120, September 2019.

  • Bhattacharya, S., Badyal, S., Wheeler, W., Gil, S., Bertsekas, D.,"Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems," IEEE Robotics and Automation Letters, to appear, 2020.


    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    

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