Are you looking for the best books on Reinforcement Learning?… If yes, then this article is for you. In this article, you will find the Best Books to Learn Reinforcement Learning for Beginners & advanced like Beginner courses, and Practice test courses. So, check these Best Books to Learn reinforcement Learning for Beginners and find the Best Books to Learn Reinforcement Learning for Beginners to Advanced according to your need.
In the previous article, I shared the Best Machine Learning Books for beginners to Advanced to read in 2022, you can go through the list and enjoy reading.
Best Books to Learn Reinforcement Learning for Beginners to Advanced to know in 2022
Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field’s key ideas and algorithms.
Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.
Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning.
Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods.
Part III has new chapters on reinforcement learning’s relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson’s wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.
With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik’s Cube), multi-agent methods, Microsoft’s TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.
In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods.
In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
- Understand the deep learning context of RL and implement complex deep learning models
- Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
- Discover Microsoft’s TextWorld environment, which is an interactive fiction games platform
- Use discrete optimization in RL to solve a Rubik’s Cube
- Teach your agent to play Connect 4 using AlphaGo Zero
- Explore the very latest deep RL research on topics including AI chatbots
- Discover advanced exploration techniques, including noisy networks and network distillation techniques
Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.
This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching.
You’ll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.
Python Reinforcement Learning: Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow
The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration.
You’ll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations.
You’ll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL.
By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems.
- Train an agent to walk using OpenAI Gym and TensorFlow
- Solve multi-armed-bandit problems using various algorithms
- Build intelligent agents using the DRQN algorithm to play the Doom game
- Teach your agent to play Connect4 using AlphaGo Zero
- Defeat Atari arcade games using the value iteration method
- Discover how to deal with discrete and continuous action spaces in various environments
This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology.
The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including:
- deep learning;
- artificial intelligence;
- applications of game theory;
- mixed modality learning; and
- multi-agent reinforcement learning.
Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.
This book considers large and challenging multistage decision problems, which can be solved in principle by dynamic programming, but their exact solution is computationally intractable. It can be used as a textbook or for self-study in conjunction with instructional videos and slides, and other supporting material, which are available from the author’s website.
The book discusses solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are known by several essentially equivalent names: reinforcement learning, approximate dynamic programming, and neuro-dynamic programming.
They underlie, among others, the recent impressive successes of self-learning in the context of games such as chess and Go. One of the aims of the book is to explore the common boundary between artificial intelligence and optimal control, and to form a bridge that is accessible by workers with background in either field.
Another aim is to organize coherently the broad mosaic of methods that have proved successful in practice while having a solid theoretical and/or logical foundation. This may help researchers and practitioners to find their way through the maze of competing ideas that constitute the current state of the art.
The mathematical style of this book is somewhat different than other books by the same author. While we provide a rigorous, albeit short, mathematical account of the theory of finite and infinite horizon dynamic programming, and some fundamental approximation methods, we rely more on intuitive explanations and less on proof-based insights. We also illustrate the methodology with many example algorithms and applications.
Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function.
This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice.
Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation.
The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty.
Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic.
There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.
Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you’ll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.
As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls.
- Model and solve complex sequential decision-making problems using RL
- Develop a solid understanding of how state-of-the-art RL methods work
- Use Python and TensorFlow to code RL algorithms from scratch
- Parallelize and scale up your RL implementations using Ray’s RLlib package
- Get in-depth knowledge of a wide variety of RL topics
- Understand the trade-offs between different RL approaches
- Discover and address the challenges of implementing RL in the real world
Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.
This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.
- Understand each key aspect of a deep RL problem
- Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)
- Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)
- Understand how algorithms can be parallelized synchronously and asynchronously
- Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
- Explore algorithm benchmark results with tuned hyperparameters
- Understand how deep RL environments are designed
And here the list ends. So, these are the Best Books to Learn Reinforcement Learning for Beginners to Advanced. I will keep adding more Best Books on Reinforcement Learning to this list.
I hope these Best Books to Learn reinforcement Learning for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.