• Title: Deep Reinforcement Learning: A Textbook
• Author(s) Aske Plaat
• Publisher: Springer (2022); eBook (Creative Commons Licensed, arXiv 2023)
• License(s): Creative Commons License (CC)
• Hardcover: 421 pages
• eBook: PDF
• Language: English
• ISBN-10: 9811906378
• ISBN-13: 978-9811906374
• Share This:  

Book Description

This book describes the foundations, the algorithms and the applications of deep reinforcement learning, covers the established model-free and model-based methods that form the basis of the field. The programming language of this book is Python.

• Aske Plaat is a Professor of Data Science at Leiden University and scientific director of the Leiden Institute of Advanced Computer Science (LIACS).

• Amazon

• Depp Learning
• Reinforcement Learning
• Machine Learning
• Statistics, Mathematical Statistics, etc.
• Probability and Stochastic Processe

• Deep Reinforcement Learning: A Textbook (Aske Plaat)
• The Mirror Site (1) - PDF (Download Only)
• Book Homepage (Slides, Exercises, etc.)
• Foundations of Deep Reinforcement Learning: Theory and Practice in Python

• An Introduction to Deep Reinforcement Learning

  Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.

• Multi-Agent Reinforcement Learning (Stefano V. Albrecht, et al.)

  The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

• Reinforcement Learning: An Introduction, 2nd Edition

  Provides a clear and simple account of the key ideas and algorithms of Reinforcement Learning (RL) that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

• Reinforcement Learning: Foundations (Shie Mannor, et al.)

  Bridging the gap between introductory texts and the specialized research literature, this self-contained book with practical applications introduces the foundations of sequential decision-making and intelligent agent design.

• Reinforcement Learning, Bit by Bit (Xiuyuan Lu, et al.)

  Reinforcement Learning (RL) agents have demonstrated remarkable achievements in simulated environments. This tutorial offers a framework that can guide associated agent design decisions.

• Mathematical Foundations of Reinforcement Learning

  This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic Reinforcement Learning (RL) algorithms. Numerous illustrative examples are included throughout.

• Reinforcement Learning with Applications in Finance

  This book aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas ― especially finance, implementing models and algorithms in well-designed Python code.

• A Course in Reinforcement Learning (Dimitri P. Bertsekas)

  The purpose of the book is to give an overview of the Reinforcement Learning (RL) methodology, with a particular focus on problems of optimal and suboptimal control, as well as discrete optimization.

• Reinforcement Learning: Theory and Algorithms (Alekh Agarwal)

  The purpose of the course is to give an overview of the Reinforcement Learning (RL) methodology, particularly as it relates to problems of optimal and suboptimal decision and control, as well as discrete optimization.

• Distributional Reinforcement Learning (Marc G. Bellemare, et al)

  Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. This first comprehensive guide provides a new mathematical formalism for thinking about decisions from a probabilistic perspective.