Reinforcement Learning and Optimal Control
by Dimitri P. Bertsekas
Publisher: Athena Scientific 2019
Number of pages: 276
Description:
The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance.
Download or read it online for free here:
Download link
(multiple PDF files)
Similar books
Algorithms for Reinforcement Learningby Csaba Szepesvari - Morgan and Claypool Publishers
We focus on those algorithms of reinforcement learning that build on the theory of dynamic programming. We give a comprehensive catalog of learning problems, describe the core ideas, followed by the discussion of their properties and limitations.
(10759 views)
A Brief Introduction to Machine Learning for Engineersby Osvaldo Simeone - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(9112 views)
An Introductory Study on Time Series Modeling and Forecastingby Ratnadip Adhikari, R. K. Agrawal - arXiv
This work presents a concise description of some popular time series forecasting models used in practice, with their features. We describe three important classes of time series models, viz. the stochastic, neural networks and SVM based models.
(14166 views)
Statistical Learning and Sequential Predictionby Alexander Rakhlin, Karthik Sridharan - University of Pennsylvania
This text focuses on theoretical aspects of Statistical Learning and Sequential Prediction. The minimax approach, which we emphasize throughout the course, offers a systematic way of comparing learning problems. We will discuss learning algorithms...
(8554 views)