Introduction

Reinforcement learning (RL) is a paradigm of AI methodologies in which an agent learns to interact with its environment in order to maximize the expectation of reward signals received from its environment. Unlike supervised learning, in which the agent is given labeled examples and learns to predict an output based on input, RL involves the agent actively taking actions in its environment and receiving feedback in the form of rewards or punishments. This feedback is used to adjust the agent’s behavior and improve its performance over time.

RL has been applied to a wide range of domains, including robotics, natural language processing, and finance. In the gaming industry, RL has been used to develop advanced game-playing agents, such as the AlphaGo [1] algorithm that defeated a human champion in the board game Go. In the healthcare industry, RL has been used to optimize treatment plans for patients with chronic diseases, such as diabetes. RL has also been used in the field of robotics, allowing robots to learn and adapt to new environments and tasks.

One of the most iconic recent breakthroughs in RL is the development of chatGPT [2] by OpenAI, a natural language processing system that can hold intelligent conversations with humans. chatGPT was trained on a large dataset of human conversations and can generate coherent and contextually appropriate responses to user inputs. This system demonstrates the potential for RL to be used to improve natural language processing systems and create more human-like AI assistants.

As RL continues to advance and make an impact in various fields, it has become increasingly important for professionals and researchers to have a strong understanding of this technique. If you’re interested in learning about RL, you’re in luck! There are a variety of resources available online that can help you get started and become proficient in this exciting field. In this blog post, we’ll highlight some of the best, mostly free, resources for learning about RL, including tutorials, courses, books, and more. Whether you’re a beginner looking to get your feet wet or an experienced practitioner looking to deepen your understanding, these resources will have something for you.

In this post, we are going to first start by introducing the best **online courses, lectures, and tutorials **available for RL on the internet. Then we will introduce the best and most popular **books **and **textbooks **in the field. And at last, we will also include some useful extra resources and GitHub repositories on the topic.

Online Courses

While there are numerous courses available on the subject, we’ve carefully selected a list of the most comprehensive and high-quality options that are mostly free. These courses cover a wide range of topics in RL, from the basics to advanced concepts, and are taught by experts in the field. Whether you’re a beginner looking to get your feet wet or an experienced practitioner looking to deepen your understanding, these courses will have something for you. Keep reading to discover some of the top online courses for learning about RL! Please note that this is not an exhaustive list, but rather a curated selection of the most highly recommended courses available.

The Reinforcement Learning Specialization on Coursera, offered by the University of Alberta and the Alberta Machine Intelligence Institute, is a comprehensive program designed to teach you the foundations of reinforcement learning. This specialization consists of three courses and one capstone project that cover a wide range of topics in RL, including RL fundamentals, value-based methods, policy gradient methods, model-based RL, deep RL, etc. Throughout the course, you’ll have the opportunity to apply what you’ve learned through hands-on programming assignments and a final project. The course is taught by experienced instructors and academics who are experts in the field of RL and includes a mix of lectures, readings, and interactive exercises. This specialization is suitable for students with a background in machine learning or a related field and is a great resource for anyone looking to gain a solid understanding of RL.

Although it is not technically free, you could always apply for Coursera’s financial aid to waive the course fee if you were not to afford it. However, considering the content quality and material, it would be totally worthwhile.

Link to the course:www.coursera.org/specializations/reinforcement-learning

2 - Reinforcement Learning Lecture Series 2021 — by DeepMind x UCL

The “Reinforcement Learning Lecture Series” is a series of lectures on the topic of reinforcement learning, presented by DeepMind and UCL. This course covers a wide range of topics within the field of reinforcement learning, including foundational concepts such as Markov decision processes and dynamic programming, as well as more advanced techniques such as model-based and model-free learning and off-policy, value-/policy-based algorithms, function approximation, and deep RL. The lectures are offered by renowned academics and researchers from Deepmind and UCL. The lectures are aimed at researchers and practitioners interested in learning about the latest developments and applications in reinforcement learning. The course is offered online and is open to anyone who is interested in learning about this exciting and rapidly-evolving field.

Link to the course: Reinforcement Learning Lecture Series 2021

There is also an older version of this series from 2018 which could be found here.

3 - Stanford CS234: Reinforcement Learning — Winter 2019

The CS234 Reinforcement Learning course from Stanford is a comprehensive study of reinforcement learning, taught by Prof. Emma Brunskill. This course covers a wide range of topics in RL, including foundational concepts such as MDPs and Monte Carlo methods, as well as more advanced techniques like temporal difference learning and deep reinforcement learning. The course is designed for students who have a background in machine learning and are interested in learning about the latest techniques and applications in reinforcement learning. The course is offered through a series of video lectures, which are available on YouTube through the provided link.

Link to the course: Here

4 - Introduction to Reinforcement Learning with David Silver

The Introduction to Reinforcement Learning with David Silver course is a comprehensive introduction to the field of reinforcement learning, taught by Professor David Silver. Silver is a leading researcher in the field of reinforcement learning and artificial intelligence, and has been a key contributor to the development of AlphaGo, the first computer program to defeat a professional human player in the game of Go. He is also among the authors of some of the key research papers in RL such as Deep Q-Learning and DDPG algorithm. The course covers the fundamental concepts and techniques of reinforcement learning, including dynamic programming, Monte Carlo methods, and temporal difference learning. It also covers more advanced topics such as exploration-exploitation trade-offs, function approximation, and deep reinforcement learning. Overall, the course provides a solid foundation in reinforcement learning and is suitable for anyone interested in learning more about this exciting and rapidly-evolving field of artificial intelligence.

Link to the course: Introduction to Reinforcement Learning with David Silver

5 - UC Berkeley CS 285: Deep Reinforcement Learning — Fall 2021

The UC Berkeley CS 285 Deep Reinforcement Learning course is a graduate-level course that covers the field of reinforcement learning, with a focus on deep learning techniques. The course is taught by Prof. Sergey Levine and is designed for students who have a strong background in machine learning and are interested in learning about the latest techniques and applications in reinforcement learning. The course covers a wide range of topics, including foundational concepts such as Markov decision processes and temporal difference learning, as well as advanced techniques like deep Q-learning and policy gradient methods. The course is offered through a series of video lectures, which are available on YouTube through the provided link.

Link to the course: Here

There is also an older series of the course from Fall 2020 here.

6 - Deep RL BootCamp — UC Berkeley

The Deep RL Bootcamp is an intensive two-day course on deep reinforcement learning, taught by leading researchers in the field. The course covers a wide range of topics, including value-based methods, policy gradient algorithms, model-based reinforcement learning, exploration and uncertainty, and deep reinforcement learning in the real world. It features a mix of lectures and hands-on exercises, giving attendees the opportunity to learn about the latest techniques and apply them to real-world problems. The course is designed for researchers and practitioners with a background in machine learning and/or reinforcement learning and is suitable for those looking to gain a deeper understanding of the field and advance their research or career in this exciting area of artificial intelligence.

Link to the course: Deep RL Bootcamp

7 - Deep Reinforcement Learning Course by HuggingFace

The Deep RL course by Hugging Face is an in-depth and interactive learning experience that covers the most important topics in deep reinforcement learning. The course is divided into units that cover various aspects of the field such as the Q-learning algorithm, policy gradients, and advanced topics like exploration, multi-agent RL, and meta-learning. Each unit includes a combination of video lectures, interactive coding tutorials, and quizzes to help learners understand and apply the concepts.

The course also includes hands-on projects that allow learners to apply their knowledge to real-world problems. These projects include creating an RL agent to play a game, training an RL agent to navigate a virtual environment, and building an RL agent to play a game of chess. These projects provide an opportunity for learners to get hands-on experience working with RL models, and gain an understanding of the challenges and complexities of working with these models.

The course also includes explanations of the theoretical foundations of RL, providing an understanding of the mathematical concepts and algorithms used in the field. The course is designed to be accessible to people with different backgrounds and levels of experience, from those new to the field to experienced practitioners. The course is taught by Simon Thomas, who is a researcher and expert in the field of deep reinforcement learning, and the course content is regularly updated to keep up with the latest advancements in the field.

Links to the course: Deep Reinforcement Learning Course Deep Reinforcement Learning Course is a free course about Deep Reinforcement Learning from beginner to expert.simoninithomas.github.io Welcome to the 🤗 Deep Reinforcement Learning Course - Hugging Face Course Welcome to the most fascinating topic in Artificial Intelligence: Deep Reinforcement Learning. This course will teach…huggingface.co GitHub - huggingface/deep-rl-class: This repo contains the syllabus of the Hugging Face Deep… This repository contains the Deep Reinforcement Learning Course mdx files and notebooks. The website is here…github.com

8 - Lectures by Pieter Abbeel

Pieter Abbeel is a renowned computer scientist and roboticist who is currently a professor at the University of California, Berkeley. He is known for his research in the field of robotics, particularly in the areas of reinforcement learning, learning from demonstration, and robot manipulation. He has made notable contributions to the field of robotic grasping and manipulation, developing algorithms for robots to learn to grasp and manipulate objects using trial-and-error.

He also has been a pioneer in the field of apprenticeship learning, which allows robots to learn from human demonstrations. He has published over 150 papers, many of which can be accessed on his personal website and also has a set of video lectures available on youtube. He has also been involved in the development of open-source software for robotics and machine learning and is the co-author of the popular open-source software library OpenAI Gym, which is widely used in the field of reinforcement learning.

His online lectures, which are available on YouTube are one of the high quality material available in reinforcement learning.

His “Foundations of Deep RL — lecture series” on his own YouTube channel:

His Lectures from CS188 Artificial Intelligence UC Berkeley, Spring 2013:

9 - Spinning Up in Deep RL by OpenAI

Spinning Up in Deep RL is developed and maintained by OpenAI. It is a resource for people who want to learn about deep reinforcement learning (RL) and how to apply it. The website provides a comprehensive introduction to RL and its algorithms and includes tutorials and guides on how to implement and run RL experiments. The website also includes a set of resources such as papers, videos, and code examples to help users learn about RL.

The website is based on the software library OpenAI Baselines, which is an implementation of RL algorithms in Python with PyTorch and TensorFlow. The library includes implementations of popular RL algorithms such as DQN, PPO, A2C, and TRPO. The website provides detailed instructions and code examples on how to use the library to train RL agents and run experiments.

The website is designed to be accessible to people with different levels of experience and provides a step-by-step guide to getting started with RL. The website is divided into sections, including an introduction to RL, tutorials on how to use the library, and a section on advanced topics such as multi-agent RL, exploration, and meta-learning. The website also provides a set of Jupyter notebooks that users can run and modify, allowing them to experiment with different RL algorithms and environments.

The link to the website: Welcome to Spinning Up in Deep RL! - Spinning Up documentation

10 - Phil Tabor’s RL Courses

Phil Tabor is a machine learning engineer and educator who specializes in the field of reinforcement learning. He is known for his practical approach to teaching and has a special focus on the hands-on aspect of the field. He has created several courses on machine learning and artificial intelligence on Udemy, with a focus on reinforcement learning. He also has a YouTube channel “Machine Learning with Phil” where he uploads videos on various reinforcement learning topics such as Q-learning, policy gradients, and more advanced topics. He also uploads code-along videos to help learners understand the concept and apply them.

His more practical approach to the field makes it rather much different than other available content. Aside from his paid courses on Udemy which are very comprehensive and well-framed, he has tons of free content on his YouTube channel which are not much less than his paid ones.

**Youtube channel: **https://www.youtube.com/@MachineLearningwithPhil

**Udemy: **https://www.udemy.com/user/phil-tabor/

Books

There are tons of great books published about reinforcement learning however 5 of the most popular and comprehensive ones are listed below:

1. Richard Sutton and Andrew Barto, “Reinforcement Learning: An Introduction” (2nd Edition) — Most Recommended*

Reinforcement Learning: An Introduction (2nd Edition) by Richard Sutton and Andrew Barto is a must-have resource for anyone interested in the field of reinforcement learning. This book provides a comprehensive introduction to the fundamental concepts and algorithms of reinforcement learning, making it an essential resource for students, researchers, and practitioners. The second edition includes new chapters on recent developments in the field and updates to existing material, making it even more current and relevant.

The book starts with an introduction to the basic concepts of RL and lays out the RL problem along with a history of the field and its relationship to other fields such as psychology, neuroscience, and control theory. It then delves into the foundational algorithms and concepts of the field, including Multiarm bandits, Markov decision processes, dynamic programming, and Monte Carlo methods.

The book also covers advanced topics such as temporal-difference learning, planning and learning with function approximators, and exploration and exploitation in reinforcement learning. Additional chapters discuss the application of reinforcement learning in various domains, including robotics, game playing, and healthcare.

The book also includes chapters on recent developments in the field such as deep reinforcement learning, policy gradient methods, and inverse reinforcement learning. The final chapters cover the challenges and future of the field, including safety and reliability, multi-agent reinforcement learning, and the role of reinforcement learning in artificial general intelligence.

Book Chapters:

  1. The Reinforcement Learning Problem
  2. Multi-arm Bandits
  3. Finite Markov Decision Processes
  4. Dynamic Programming
  5. Monte Carlo Methods
  6. Temporal-Difference Learning
  7. Eligibility Traces
  8. Planning and Learning with Tabular Methods
  9. On-policy Approximation of Action Values
  10. Off-policy Approximation of Action Values
  11. Policy Approximation
  12. Psychology
  13. Neuroscience
  14. Applications and Case Studies
  15. Prospects

2. Mykel J. Kochenderfer, “Decision Making Under Uncertainty: Theory and Application”

Decision Making Under Uncertainty: Theory and Application, by Mykel J. Kochenderfer, is a comprehensive guide to decision-making under uncertainty, with a focus on reinforcement learning. The book covers the fundamental concepts of decision theory, Markov decision processes, and reinforcement learning algorithms, providing the reader with a solid foundation in these areas.

The book also delves into advanced topics such as planning under uncertainty, safe reinforcement learning, and the use of decision-making methods in real-world applications. The author explains the concepts in a clear and concise manner, with the help of examples and exercises to help the reader understand and apply the material.

The book is intended for a broad audience, including researchers and practitioners in the fields of artificial intelligence, operations research, and control systems. It’s also suitable for advanced undergraduate and graduate students in these areas. The book provides a thorough introduction to the theory and application of decision-making under uncertainty, with a focus on reinforcement learning, making it an essential resource for anyone interested in this field.

Book Chapters:

  1. Introduction
  2. Probabilistic Models
  3. Decision Problems
  4. Sequential Problems
  5. Model Uncertainty
  6. State Uncertainty
  7. Cooperative Decision Making
  8. Probabilistic Surveillance Video Search
  9. Dynamic Models for Speech Applications
  10. Optimized Airborne Collision Avoidance
  11. Multi-agent Planning for Persistent Surveillance
  12. Integrating Automation with Humans

3. Phil Winder, “Reinforcement Learning”

“Reinforcement Learning” by Phil Winder is an in-depth examination of one of the most exciting and rapidly growing areas of machine learning. The book provides a comprehensive introduction to the theory and practice of reinforcement learning, covering a wide range of topics that are essential for understanding and working with this powerful technique.

The book starts with the fundamentals of Markov decision processes, which form the mathematical foundation of reinforcement learning. It then delves into Q-learning, a popular algorithm for finding the optimal action-value function in a given environment. The book also covers policy gradients, a class of algorithms that allow for the optimization of policies directly, rather than value functions. Additionally, it covers the recent advancements in deep reinforcement learning and how it can be applied to solve complex problems.

The book also includes numerous practical examples and exercises that help readers apply the concepts to real-world problems. This book is ideal for machine learning practitioners, researchers, and students who are interested in understanding and working with reinforcement learning. It provides a clear and accessible introduction to the field, making it an essential resource for anyone looking to get started with reinforcement learning or deepen their understanding of this powerful technique.

Book Chapters:

  1. Why Reinforcement Learning?
  2. Markov Decision Processes, Dynamic Programming, and Monte Carlo Methods
  3. Temporal-Difference Learning, Q-Learning, and n-Step Algorithms
  4. Deep Q-Networks
  5. Policy Gradient Methods
  6. Beyond Policy Gradients
  7. Learning All Possible Policies with Entropy Methods
  8. Improving How an Agent Learns
  9. Practical Reinforcement Learning
  10. Operational Reinforcement Learning
  11. Conclusions and the Future

4. Alexander Zai and Brandon Brown, “Deep Reinforcement Learning in Action”

“Deep Reinforcement Learning in Action” by Alexander Zai and Brandon Brown is an in-depth guide that takes the reader through the process of building intelligent systems using deep reinforcement learning. The book starts by introducing the basic concepts and algorithms of reinforcement learning, including Q-learning and policy gradients. It then goes on to cover more advanced topics such as actor-critic methods and deep Q-networks (DQN), which are used to improve the performance of reinforcement learning algorithms.

One of the key features of the book is its emphasis on hands-on examples and exercises. Throughout the book, the authors provide code snippets and sample projects that illustrate how to implement reinforcement learning algorithms in practice. These examples and exercises are designed to help readers understand the material and apply it to their own projects.

In addition to covering the fundamentals of reinforcement learning, the book also covers recent advances in the field such as double DQN, prioritized replay, and A3C. These techniques are used to improve the performance of reinforcement learning algorithms and make them more efficient. The book is intended for readers with some experience in machine learning and deep learning, but no prior experience with reinforcement learning is required. The authors provide a comprehensive and accessible introduction to the field, making it an ideal choice for both beginners and experienced practitioners.

Book Chapters:

  1. What is reinforcement learning
  2. Modeling reinforcement learning problems: Markov decision processes
  3. Predicting the best states and actions: Deep Q-networks
  4. Learning to pick the best policy: Policy gradient methods
  5. Tackling more complex problems with actor-critic methods
  6. Alternative optimization methods: Evolutionary algorithms
  7. Distributional DQN: Getting the full story
  8. Curiosity-driven exploration
  9. Multi-agent reinforcement learning
  10. Interpretable reinforcement learning: Attention and relational model
  11. conclusion: A review and roadmap

5. Maxim Lapan, “Deep Reinforcement Learning Hands-On”

Deep Reinforcement Learning Hands-On” by Maxim Lapan is an updated edition of the popular guide to understanding and implementing deep reinforcement learning (DRL) techniques. This book is designed to provide readers with a solid understanding of the key concepts and techniques behind DRL and to equip them with the practical skills needed to build and train their own DRL models.

The book covers a wide range of topics, including the basics of reinforcement learning and its connection to neural networks, advanced DRL algorithms such as Q-Learning, SARSA, and DDPG, and the use of DRL in real-world applications such as robotics, gaming, and autonomous vehicles. Additionally, the book includes practical examples and hands-on exercises, allowing readers to apply the concepts and techniques covered in the book to real-world problems.

With its focus on both theory and practice, “Deep Reinforcement Learning Hands-On” is the perfect guide for anyone looking to gain a deep understanding of DRL and start building their own DRL models.

Book Chapters:

  1. What Is Reinforcement Learning?
  2. OpenAI Gym
  3. Deep Learning with PyTorch
  4. The Cross-Entropy Method
  5. Tabular Learning and the Bellman Equation
  6. Deep Q-Networks
  7. Higher-Level RL Libraries
  8. DQN Extensions
  9. Ways to Speed up RL
  10. Stocks Trading Using RL
  11. Policy Gradients — an Alternative
  12. The Actor-Critic Method
  13. Asynchronous Advantage Actor-Critic
  14. Training Chatbots with RL
  15. The TextWorld Environment
  16. Web Navigation
  17. Continuous Action Space
  18. RL in Robotics
  19. Trust Regions — PPO, TRPO, ACKTR, and SAC
  20. Black-Box Optimization in RL
  21. Advanced Exploration
  22. Beyond Model-Free — Imagination
  23. AlphaGo Zero
  24. RL in Discrete Optimization
  25. Multi-agent RL

Bonus: Other Useful Resources

The Best Tools for Reinforcement Learning in Python

This post by neptune.ai provides an overview of the popular tools and libraries used in RL with Python to help readers decide which tools are best suited for their specific use case. it covers a variety of popular RL libraries such as TensorFlow, PyTorch, and OpenAI Baselines, as well as other tools such as OpenAI Gym, and RL Toolbox. The post also covers other topics such as visualization tools, model management tools and experiment tracking tools which are useful for RL. The blog post is well-organized and easy to follow. It includes code examples and links to the relevant documentation for each tool, making it a useful resource for anyone interested in getting started with RL in Python. The Best Tools for Reinforcement Learning in Python You Actually Want to Try - neptune.ai Nowadays, Deep Reinforcement Learning (RL) is one of the hottest topics in the Data Science community. The fast…neptune.ai

awesome-deep-rl

This GitHub repository is a curated list of resources for deep reinforcement learning (RL) and contains a comprehensive list of papers, tutorials, videos, and other resources on various topics related to deep RL, such as Q-learning, policy gradients, exploration, meta-learning, and more. It also includes links to popular RL libraries and frameworks, such as TensorFlow, PyTorch, and OpenAI Baselines, as well as other tools and resources that are useful for RL. The repository is well-organized and easy to navigate, making it a useful resource for anyone interested in learning about deep RL. GitHub - kengz/awesome-deep-rl: A curated list of awesome Deep Reinforcement Learning resources. A curated list of awesome Deep Reinforcement Learning resources. - GitHub - kengz/awesome-deep-rl: A curated list of…github.com

Awesome Deep Reinforcement Learning in Finance

this article provides an overview of the use of deep reinforcement learning (RL) in the field of finance. The article includes a curated list of resources for learning more about RL in finance, including papers, videos, and tutorials. The article discusses the potential applications of RL in finance such as portfolio management, algorithmic trading, and risk management. It also highlights some of the challenges and limitations of using RL in finance, such as the lack of data and the difficulty of evaluating the performance of RL models. Awesome Deep Reinforcement Learning in Finance

Refernces

[1] — Silver, D., Huang, A., Maddison, C. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016). https://doi.org/10.1038/nature16961

[2] — https://openai.com/blog/chatgpt/


Author: Ebrahim Pichka