Logo

Algorithms for Reinforcement Learning

Large book cover: Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning
by

Publisher: Morgan and Claypool Publishers
ISBN/ASIN: 1608454924
Number of pages: 98

Description:
In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.

Home page url

Download or read it online for free here:
Download link
(1.6MB, PDF)

Similar books

Book cover: Statistical Learning and Sequential PredictionStatistical Learning and Sequential Prediction
by - 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...
(7070 views)
Book cover: Machine Learning and Data Mining: Lecture NotesMachine Learning and Data Mining: Lecture Notes
by - University of Toronto
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.
(10483 views)
Book cover: Modeling Agents with Probabilistic ProgramsModeling Agents with Probabilistic Programs
by - AgentModels.org
This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning. One motivation is to create richer models of human planning, which capture human biases. The book assumes basic programming experience.
(6216 views)
Book cover: An Introductory Study on Time Series Modeling and ForecastingAn Introductory Study on Time Series Modeling and Forecasting
by - 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.
(12510 views)