Rollout, Policy Iteration, and Distributed Reinforcement Learning

by Dimitri P. Bertsekas

ISBN: 978-1-886529-07-6
Publication: 2020, 376 pages, hardcover
Price: $89.00
AVAILABLE

Contents, Preface

Video Course from ASU, and other Related Material

Ordering, Home


rolloutcover_medsize.jpg

The purpose of this book is to develop in greater depth some of the methods from the author's Reinforcement Learning and Optimal Control recently published textbook (Athena Scientific, 2019). In particular, we present new research, relating to systems involving multiple agents, partitioned architectures, and distributed asynchronous computation. We pay special attention to the contexts of dynamic programming/policy iteration and control theory/model predictive control. We also discuss in some detail the application of the methodology to challenging discrete/combinatorial optimization problems, such as routing, scheduling, assignment, and mixed integer programming, including the use of neural network approximations within these contexts.

The book focuses on the fundamental idea of policy iteration, i.e., start from some policy, and successively generate one or more improved policies. If just one improved policy is generated, this is called rollout, which, based on broad and consistent computational experience, appears to be one of the most versatile and reliable of all reinforcement learning methods. In this book, rollout algorithms are developed 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.

Approximate policy iteration is more ambitious than rollout, but it is a strictly off-line method, and it is generally far more computationally intensive. This motivates the use of parallel and distributed computation. One of the purposes of the monograph is to discuss distributed (possibly asynchronous) methods that relate to rollout and policy iteration, both in the context of an exact and an approximate implementation involving neural networks or other approximation architectures.

Much of the new research is inspired by the remarkable AlphaZero chess program, where policy iteration, value and policy networks, approximate lookahead minimization, and parallel computation all play an important role.

This book relates to several of our other books: Reinforcement Learning and Optimal Control (Athena Scientific, 2019), Neuro-Dynamic Programming (Athena Scientific, 1996), Dynamic Programming and Optimal Control (4th edition, Athena Scientific, 2017), Abstract Dynamic Programming (2nd edition, Athena Scientific, 2018), and Nonlinear Programming (3rd edition, Athena Scientific, 2016).

The mathematical style of this book is somewhat different than the Neuro-Dynamic Programming book. While we provide a rigorous, albeit short, mathematical account of the theory of finite and infinite horizon dynamic programming, and some fundamental approximation methods, we rely more on intuitive explanations and less on proof-based insights. Moreover, our mathematical requirements are quite modest: calculus, a minimal use of matrix-vector algebra, and elementary probability (mathematically complicated arguments involving laws of large numbers and stochastic convergence are bypassed in favor of intuitive explanations).

Among its special features, the book:

The author is McAfee Professor of Engineering at the Massachusetts Institute of Technology and a member of the prestigious US National Academy of Engineering. He is the recipient of the 2001 A. R. Raggazini ACC education award, the 2009 INFORMS expository writing award, the 2014 Kachiyan Prize, the 2014 AACC Bellman Heritage Award, the 2015 SIAM/MOS George B. Dantsig Prize. In 2018, he shared the John von Neumann INFORMS theory award with John Tsitsiklis for the books "Neuro-Dynamic Programming", and "Parallel and Distributed Computation".


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 Value Iteration Algorithms in Dynamic Programming and Reinforcement Learning," ASU Report, April 2020.

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

  • D. P. Bertsekas, "Constrained Multiagent Rollout and Multidimensional Assignment with the Auction Algorithm," arXiv preprint, arXiv:2002.07407 February 2020.

  • Bhattacharya, S., Sahil 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.

  • D. P. Bertsekas, "Biased Aggregation, Rollout, and Enhanced Policy Improvement for Reinforcement Learning," Lab. for Information and Decision Systems Report, MIT, October 2018; a shorter version appears as arXiv preprint arXiv:1910.02426, Oct. 2019.

  • D. P. Bertsekas, "Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations," Lab. for Information and Decision Systems Report, MIT, April 2018 (revised August 2018); arXiv preprint arXiv:1804.04577; a version published in IEEE/CAA Journal of Automatica Sinica. (Lecture Slides). (Related Video Lecture).


    [Return to Athena Scientific Homepage]