Which Reinforcement Learning books have the most practical and engaging examples and exercises?
Reinforcement Learning (RL) is a branch of machine learning that focuses on learning from trial and error, rewards and penalties, and interaction with the environment. It is widely used in various domains such as robotics, games, self-driving cars, and recommendation systems. However, learning RL can be challenging, as it requires a solid foundation of mathematics, algorithms, and programming skills. Moreover, it can be hard to grasp the intuition and logic behind the RL concepts and methods without seeing them in action. That's why reading books that provide practical and engaging examples and exercises can be very helpful for aspiring and experienced RL practitioners. In this article, we will review some of the best RL books that offer hands-on and interactive learning opportunities.
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Mohammed BahageelData Scientist / Data Analyst | Machine Learning | Deep Learning | Artificial Intelligence | Data Analytics…
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Haroon AnsariApplied Research @ LinkedIn | Indian Institute of Science (IISc Bangalore) | NLP | Deep RL
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Dr. Jayashree Rajesh PrasadCertNexus Certified AI Practitioner, IBM Data Analyst, SAP Technology Consultant, Senior member IEEE, Chair IEEE CIS…
This book is considered a classic and comprehensive reference for RL, written by two of the most influential researchers in the field, Richard S. Sutton and Andrew G. Barto. It covers the core concepts, methods, and algorithms of RL, as well as some advanced topics such as planning, exploration, and function approximation. The book also provides many illustrative examples and exercises that help readers understand and apply the RL theory to various problems. For example, the book shows how to use RL to play tic-tac-toe, blackjack, gridworld, mountain car, and inverted pendulum. The book also comes with a website that contains code examples, solutions, and additional resources.
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Mohammed Bahageel
Data Scientist / Data Analyst | Machine Learning | Deep Learning | Artificial Intelligence | Data Analytics |Reinforcement Learning | Data Visualization | Python | R | Julia | JavaScript | Front-End Development
Best Reinforcement Learning Books with Practical Examples" by Sudharsan Ravichandiran is a comprehensive guide to RL with Python. It covers essential concepts and algorithms like Markov decision processes, Q-learning, SARSA, and DQN. The book explores advanced topics and applications such as policy gradients, actor-critic methods, and multi-agent RL. It includes numerous code examples and exercises to help readers implement and test RL algorithms on various environments and tasks
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Haroon Ansari
Applied Research @ LinkedIn | Indian Institute of Science (IISc Bangalore) | NLP | Deep RL
This book is a good starting point for anyone interested in reinforcement learning and wants to build a good conceptual foundation for it. Although the book doesn't delve into the deeper mathematical structures of algorithms, it has good coverage of topics in RL that are helpful in understanding advanced algorithms and concepts in DeepRL. One can go through the book and start reading key papers in RL ( can be found on spinning up website of open AI).
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Dr. Jayashree Rajesh Prasad
CertNexus Certified AI Practitioner, IBM Data Analyst, SAP Technology Consultant, Senior member IEEE, Chair IEEE CIS, Advisor CIS-SB, Member IEEE EXECOM, Professor & Head, CSE AIA, SoC MIT ADT University
Would love to recommend this one which is my favourite. "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto Overview: Often referred to as the "RL bible," this book provides a thorough introduction to the field, balancing theory and practical examples. Practicality: The second edition includes more detailed examples and exercises, including programming exercises that help solidify the concepts discussed. Engaging Examples: Uses illustrative examples such as the k-armed bandit problem and gridworld.
This book is a practical and accessible guide to deep reinforcement learning (DRL), which is a combination of RL and deep learning. It is written by Miguel Morales, a senior AI engineer and instructor at Lockheed Martin. The book assumes some basic knowledge of Python, machine learning, and neural networks, and teaches readers how to use DRL to solve complex and realistic problems. The book follows a learn-by-doing approach, where each chapter introduces a new concept or technique, explains it with clear diagrams and code snippets, and then applies it to a fun and challenging project. For example, the book shows how to use DRL to play Atari games, control a lunar lander, navigate a maze, and train a bipedal robot.
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Dr. Jayashree Rajesh Prasad
CertNexus Certified AI Practitioner, IBM Data Analyst, SAP Technology Consultant, Senior member IEEE, Chair IEEE CIS, Advisor CIS-SB, Member IEEE EXECOM, Professor & Head, CSE AIA, SoC MIT ADT University
This one Focuses on providing a comprehensive understanding of RL concepts along with practical implementation. "Reinforcement Learning with Python: A Complete Guide with Exercises" by Abhishek Nandy and Manisha Biswas Practicality: Includes numerous exercises and coding challenges. Engaging Examples: Covers a variety of applications such as game playing and stock trading, making the examples relevant and engaging.
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Maria Anna Skordeli
Supervisor @ BETA CAE Systems | PhD, Team Leadership
I find Reinforcement Learning to be one of the most exciting fields as it models life itself. At the same time its theoretical subtleties are challenging and the math may seem intimidating at first look. This book succeeds in all levels to help you enter the wonderful world of RL: it is very fun to read, practical and still concise in its theoretical information without being too math-heavy. Upon finishing it you will have a solid understanding of the concepts, a good intuition about how problems are modelled and algorithms work and adequate hands-on examples to help you directly put everything to practice. You will also be able to interpret the basic math to dig deeper into RL literature.
This book is actually a video course that teaches RL through interactive and visual lessons. It is created by Phil Tabor, a data scientist and founder of Machine Learning with Phil. The course assumes some familiarity with Python and machine learning, and covers the fundamentals of RL, such as value functions, policies, dynamic programming, Monte Carlo methods, temporal difference learning, and Q-learning. The course also introduces some DRL techniques, such as deep Q-networks, policy gradients, and actor-critic methods. The course uses PyTorch as the main framework, and provides many code examples and exercises that let readers experiment with RL on various environments, such as Frozen Lake, Cart Pole, Mountain Car, and Pong.
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Dr. Jayashree Rajesh Prasad
CertNexus Certified AI Practitioner, IBM Data Analyst, SAP Technology Consultant, Senior member IEEE, Chair IEEE CIS, Advisor CIS-SB, Member IEEE EXECOM, Professor & Head, CSE AIA, SoC MIT ADT University
would also like to add this one too. "Hands-On Reinforcement Learning with Python" by Sudharsan Ravichandiran Overview: Aimed at those who prefer a hands-on approach, this book walks through implementing RL algorithms from scratch using Python and popular libraries like TensorFlow and Keras. Practicality: Each chapter includes practical exercises and projects, such as building a self-driving car in a simulated environment. Engaging Examples: Real-world examples and end-to-end projects that apply RL to practical problems.
This book is a practical and comprehensive guide to RL with Python, written by Sudharsan Ravichandiran, a data scientist and author of several books on machine learning. The book covers the essential concepts and algorithms of RL, such as Markov decision processes, Bellman equations, dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, and DQN. The book also explores some advanced topics and applications of RL, such as policy gradients, actor-critic methods, A3C, DDPG, TRPO, PPO, multi-agent RL, inverse RL, and generative adversarial imitation learning. The book provides many code examples and exercises that help readers implement and test the RL algorithms on various environments and tasks, such as Blackjack, Cliff Walking, Taxi, Cart Pole, Mountain Car, Lunar Lander, Bipedal Walker, Flappy Bird, Doom, and Car Racing.
This book is a hands-on and step-by-step guide to RL with TensorFlow, written by Sayon Dutta, a software engineer and researcher at IBM. The book covers the basics of RL, such as value functions, policies, dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, and DQN. The book also introduces some DRL techniques, such as policy gradients, actor-critic methods, A3C, DDPG, TRPO, PPO, and D4PG. The book provides many code examples and exercises that help readers build and train the RL models with TensorFlow on various environments and tasks, such as Frozen Lake, Cart Pole, Mountain Car, Lunar Lander, Pendulum, Bipedal Walker, Breakout, Pong, and Half Cheetah.
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Mohammed Bahageel
Data Scientist / Data Analyst | Machine Learning | Deep Learning | Artificial Intelligence | Data Analytics |Reinforcement Learning | Data Visualization | Python | R | Julia | JavaScript | Front-End Development
Sutton and Barto RL book is a classic book that covers theory and practical aspects of reinforcement learning. "Deep Reinforcement Learning" by Abbeel and Schulman focuses on deep learning techniques and provides practical examples using popular frameworks. "Hands-On Reinforcement Learning with Python" by Ravichandiran offers hands-on examples using Python. "Reinforcement Learning: State-of-the-Art" edited by Wiering and van Otterlo features chapters by leading researchers with practical examples. "Deep Reinforcement Learning Hands-On" by Lapan focuses on deep reinforcement learning using OpenAI Gym. Each book has its own strengths, so it's recommended to explore reviews and sample chapters to find the one that suits your preferences.
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Anup Sreekumar
Applied AI in manufacturing and transportation | Organizational Culture change | Engineering Program Management
The book Sutton and Barto was a great introduction for me. I also went through the stanford course websites on AI (That covers lot of RL) , DRL ( CS 224), Berkley CS 225 DRL course site. Youtube has a ton of videos on the same - come cover math, some cover implementation, some gives great visualization to the framework. Its sad that there is not one resouce yet, but an assimilation of resources. Would also recommend Deepmind's coursework (in YT as well). It has been great learning RL/DRL and its just the beginning.
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