Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)

✍ Scribed by Richard S. Sutton, Andrew G. Barto

Publisher: The MIT Press
Year: 1998
Tongue: English
Leaves: 334
Series: Adaptive Computation and Machine Learning
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

I am a software developer and worked on applying Reinforcement Learning (RL) in cognitive fields for my patent work (pending).

This book is highly regarded in RL literature and is probably one of the few hand counted books that explicitly address RL as a subject. The book has good balance between subject matter and theory which makes it unique.

However this book has many serious drawbacks. Had there been excellent books on this subject I would have discouraged you to have this one. Rather my advice would be referring to the book "Approximate Dynamic Programming" by "Warren B. Powell" as well. This DP book has formalized the terms used in Sutton's book. This might save you from the ambiguous terminologies used in Sutton's book.

You might like to refer to these two excellent & precise works by Abhijjit Gosavi: //web.mst.edu/ ~gosavia/ tutorial.pdf and //web.mst.edu/ ~gosavia/ joc.pdf.

📜 SIMILAR VOLUMES

Distributional Reinforcement Learning (A

📁 Distributional Reinforcement Learning (Adaptive Computation and Machine Learning)

✍ Marc G. Bellemare, Will Dabney, Mark Rowland 📂 Library 📅 2023 🏛 The MIT Press 🌐 English

The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions.

Distributional Reinforcement Learning (A

📁 Distributional Reinforcement Learning (Adaptive Computation and Machine Learning)

✍ Marc G. Bellemare 📂 Library 📅 2023 🏛 MIT Press 🌐 English

The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common

Introduction to Statistical Relational L

📁 Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series)

✍ Lise Getoor; Ben Taskar 📂 Library 📅 2007 🏛 MIT Press 🌐 English

Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and p

Introduction to Machine Learning, Second

📁 Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning)

✍ Ethem Alpaydin 📂 Library 📅 2010 🏛 The MIT Press 🌐 English

I've read several parts of chapters which concerned my work and skimmed other chapters faster. This book should serve as a starting point and mostly as a quick introduction in a subject. However, i've found this book to be useful in other way - it is compact and I found several basic reasonements an

Introduction to Machine Learning, Second

📁 Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning)

✍ Ethem Alpaydın 📂 Library 📅 2010 🏛 The MIT Press 🌐 English

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer

Introduction to Statistical Relational L

📁 Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)

✍ Lise Getoor, Ben Taskar 📂 Library 📅 2007 🌐 English

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications.