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- Title: Reinforcement Learning, Bit by Bit
- Author(s) Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen
- Publisher: Now Publishers (2023); eBook (Creative Commons Licensed)
- License(s): Creative Commons License (CC)
- Hardcover: 150 pages
- eBook: HTML and PDF
- Language: English
- ISBN-10: 1638282544
- ISBN-13: 978-1638282549
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Reinforcement Learning (RL) agents have demonstrated remarkable achievements in simulated environments. This tutorial offers a framework that can guide associated agent design decisions.
About the Authors- N/A
- Reinforcement Learning
- Machine Learning
- Neural Networks and Depp Learning
- Artificial Intelligence
- Statistics, Mathematical Statistics, and SAS Programming
- Probability and Stochastic Processe
- Reinforcement Learning, Bit by Bit (Xiuyuan Lu, et al.)
- The Mirror Site (1) - HTML
- The Mirror Site (2) - PDF
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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.
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Mathematical Foundations of Reinforcement Learning (Shiyu Zhao)
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.
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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.
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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.
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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.
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Algorithms for Reinforcement Learning (Csaba Szepesvari)
This book focuses on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. It gives a fairly comprehensive catalog of learning problems, describe the core ideas, etc.
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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.
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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.
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Reinforcement Learning and Optimal Control (Dimitri Bertsekas)
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.







