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Best Reinforcement Learning Tutorials, Examples, Projects, and Courses

In reinforcement learning, your system learns how to interact intuitively with the environment by basically doing stuff and watching what happens – but obviously, there’s a lot more to it.

If you’re interested in RL, this article will provide you with a ton of new content to explore this concept. A lot of work has been done with reinforcement learning in the past few years, and I’ve collected some of the most interesting articles, videos, and use cases presenting different concepts, approaches, and methods.

In this list, you’ll find: 

  • reinforcement learning tutorials, 
  • examples of where to apply reinforcement learning,
  • interesting reinforcement learning projects,
  • courses to master reinforcement learning.

All this content will help you go from RL newbie to RL pro.

Reinforcement learning tutorials

1. RL with Mario Bros – Learn about reinforcement learning in this unique tutorial based on one of the most popular arcade games of all time – Super Mario.

2. Machine Learning for Humans: Reinforcement Learning – This tutorial is part of an ebook titled ‘Machine Learning for Humans’. It explains the core concept of reinforcement learning. There are numerous examples, guidance on the next step to follow in the future of reinforcement learning algorithms, and an easy-to-follow figurative explanation.

3. An introduction to Reinforcement Learning – There’s a lot of knowledge here, explained with much clarity and enthusiasm. It starts with an overview of reinforcement learning with its processes and tasks, explores different approaches to reinforcement learning, and ends with a fundamental introduction of deep reinforcement learning.

4. Reinforcement Learning from scratch – This article will take you through the author’s process of learning RL from scratch. The author has a lot of knowledge of deep reinforcement learning from working at Unity Technologies. Even beginners will be able to understand his overview of the core concepts of reinforcement learning.

5. Deep Reinforcement Learning for Automated Stock Trading – Here you’ll find a solution to a stock trading strategy using reinforcement learning, which optimizes the investment process and maximizes the return on investment. The article includes a proper explanation of three combined algorithms: Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The best of each algorithm is coordinated to provide a solution to optimized stock trading strategies.

6. Applications of Reinforcement Learning in Real World – Explore how reinforcement learning frameworks are undervalued when it comes to devising decision-making models. A detailed study of RL applications in real-world projects, explaining what a reinforcement learning framework is, and listing its use-cases in real-world environments. It narrows down the applications to 8 areas of learning, consisting of topics like machine learning, deep learning, computer games, and more. The author also explores the relationship of RL with other disciplines and discusses the future of RL.

7. Practical RL – This GitHub repo is an open-source course on reinforcement learning, taught on several college campuses. The repo is maintained to support online students with the option of two locales – Russian and English. The course features services like chat rooms, gradings, FAQs, feedback forms, and a virtual course environment. The course syllabus covers everything from the basics of RL to discussing and implementing different models, methods, and much more.

8. Simple Reinforcement Learning with Tensorflow Part 0: Q-Learning with Tables and Neural Networks – The first part of a tutorial series about reinforcement learning with TensorFlow. The author explores Q-learning algorithms, one of the families of RL algorithms. The simple tabular look-up version of the algorithm is implemented first. The detailed guidance on the implementation of neural networks using the Tensorflow Q-algorithm approach is definitely worth your interest.

Examples of where to apply reinforcement learning

1. Rocket engineering – Explore how reinforcement learning is used in the field of rocket engine development. You’ll find a lot of valuable information on the use of machine learning in manufacturing industries. See why reinforcement learning is favored over other machine learning algorithms when it comes to manufacturing rocket engines.

2. Traffic Light Control – This site provides multiple research papers and project examples that highlight the use of core reinforcement learning and deep reinforcement learning in traffic light control. It has tutorials, datasets, and relevant example papers that use RL as a backbone so that you can make a new finding of your own.

3. Marketing and advertising – See how to make an AI system learn from a pre-existing dataset which may be infeasible or unavailable, and how to make AI learn in real-time by creating advertising content. This is where they have made use of reinforcement learning. 

4. Reinforcement Learning in Marketing | by Deepthi A R – This example focuses on the changing business dynamics to which marketers need to adapt. The AI equipped with a reinforcement learning scheme can learn from real-time changes and help devise a proper marketing strategy. This article highlights the changing business environment as a problem and reinforcement learning as a solution to it.

5. Robotics – This video demonstrates the use of reinforcement learning in robotics. The aim is to show the implementation of autonomous reinforcement learning agents for robotics. A prime example of using reinforcement learning in robotics.

6. Recommendation – Recommendation systems are widely used in eCommerce and business sites for product advertisement. There’s always a recommendation section displayed in many popular platforms such as YouTube, Google, etc. The ability of AI to learn from real-time user interactions, and then suggest them content, would not have been possible without reinforcement learning. This article shows the use of reinforcement learning algorithms and practical implementations in recommendation systems.

7. Healthcare – Healthcare is a huge industry with many state-of-the-art technologies bound to it, where the use of AI is not new. The main question here is how to optimize AI in healthcare, and make it learn based on real-time experiences. This is where reinforcement learning comes in. Reinforcement learning has undeniable value for healthcare, with its ability to regulate ultimate behaviors. With RL, healthcare systems can provide more detailed and accurate treatment at reduced costs.

8. NLP – This article shows the use of reinforcement learning in combination with Natural Language Processing to beat a question and answer adventure game. This example might be an inspiration for learners engaged in Natural Language Processing and gaming solutions.

9. Trading – Deep reinforcement learning is a force to reckon with when it comes to the stock trading market. The example here demonstrates how deep reinforcement learning techniques can be used to analyze the stock trading market, and provide proper investment reports. Only an AI equipped with reinforcement learning can provide accurate stock market reports.

Interesting reinforcement learning projects

1. CARLA – CARLA is an open-source simulator for autonomous driving research. The main objective of CARLA is to support the development, training, and validation of autonomous driving systems. With a package of open-source code and protocols, CARLA provides digital assets that are free to use. The CARLA eco-system also integrates code for running Conditional Reinforcement Learning models, with standalone GUI, to enhance maps with traffic lights and traffic signs information.

2. Deep Learning Flappy Bird – If you want to learn about deep Q learning algorithms in an interesting way, then this GitHub repo is for you. The project uses a Deep Q-Network to learn how to play Flappy Bird. It follows the concept of the Deep Q learning algorithm which is in the family of reinforcement learning.

3. Tensorforce – This project delivers an open-source deep reinforcement learning framework specialized in modular flexible library design and direct usability for applications in research and practice. It is built on top of Google’s Tensorflow framework. It houses high-level design implementation such as modular component-based design, separation of RL algorithm and application, and full-on TensorFlow models.

4. Ray – Ray’s main objective is to provide universal APIs for building distributed applications. This project makes use of the RLlib package, which is a scalable Reinforcement Learning library that accelerates machine learning workloads.

5. Neurojs – JavaScript is popular, and a must for developing websites. What if you need to incorporate reinforcement learning in your JS web project? Say hello to Neurojs, a JavaScript framework for deep learning in the browser using reinforcement learning. It can also perform some neural network tasks as well.

6. Mario AI – This one will definitely grab your interest if you are looking for a project with reinforcement learning algorithms for simulating games. Mario AI offers a coding implementation to train a model that plays the first level of Super Mario World automatically, using only raw pixels as the input. The algorithm applied is a deep Q-learning algorithm in the family of reinforcement learning algorithms.

7. Deep Trading Agent – Open-source project offering a deep reinforcement learning based trading agent for Bitcoin. The project makes use of the DeepSense Network for Q function approximation. The goal is to simplify the trading process using a reinforcement learning algorithm optimizing the Deep Q-learning agent. It can be a great source of knowledge.

8. Pwnagotchi – This project will blow your mind if you are into cracking Wifi networks using deep reinforcement learning techniques. Pwnagotchi is a system that learns from its surrounding Wi-Fi environment to maximize the crackable WPA key material it captures. Unlike most reinforcement learning-based systems, Pwnagotchi amplifies its parameters over time to get better at cracking WiFi networks in the environments you expose it to.

Courses to master reinforcement learning 

1. Reinforcement Learning Specialization (Coursera) – One of the best courses available in the market. With a total rating of 4.8 stars and 21000+ students already enrolled, this course will help you master the concepts of reinforcement learning. You will learn how to implement a complete RL solution and take note of its application to solve real-world problems. By the end of this course,  you will be able to formalize tasks as a reinforcement learning problem and its due solutions, understand the concepts of RL algorithms, and how RL fits under the broader umbrella of machine learning.

2. Reinforcement Learning in Python (Udemy) This is a premium course offered by Udemy at the price of 29.99 USD. It has a rating of 4.5 stars overall with more than 39,000 learners enrolled. This course is a learning playground for those who are seeking to implement an AI solution with reinforcement learning engaged in Python programming. Through theoretical and practical implementations, you will learn to apply gradient-based supervised machine learning methods to reinforcement learning, programming implementations of numerous reinforcement learning algorithms, and also know the relationship between RL and psychology.

3. Practical Reinforcement Learning (Coursera) – With a rating of 4.2,  and 37,000+learners, this course is the essential section of the Advanced Machine Learning Specialization. You are guaranteed to get knowledge of practical implementation of RL algorithms. You’ll get insights on the foundations of RL methods, and using neural network technologies for RL. One interesting part is training neural networks to play games on their own using RL.

4. Understanding Algorithms for Reinforcement Learning – If you are a total beginner in the field of Reinforcement learning then this might be the best course for you. With an overall rating of 4.0 stars and a duration of nearly 3 hours in the PluralSight platform, this course can be a quick way to get yourself started with reinforcement learning algorithms. You’ll get deep information on algorithms for reinforcement learning, basic principles of reinforcement learning algorithms, RL taxonomy, and RL family algorithms such as Q-learning and SARSA.

5. Reinforcement Learning by Georgia Tech (Udacity) – One of the best free courses available, offered by Georgia Tech through the Udacity platform. The course is formulated for those seeking to understand the world of Machine learning and Artificial Intelligence from a theoretical perspective. It provides rich insights into recent research on reinforcement learning, which will help you explore automated decision-making models.

6. Reinforcement Learning Winter (Stanford Education) – This course is provided by Stanford University as a winter session. There are some basic requirements for the course, such as Python programming proficiency, knowledge of linear algebra and calculus, basics of statistics and probability, and basics of machine learning. This course provides state of the art lectures. In the end, you will be able to define key features of RL, applications of RL on real-world problems, coding implementations of RL algorithms, and have deep knowledge of RL algorithms. This course is suited for those seeking advanced-level learning resources on the RL ecosystem.

7. Advanced AI: Deep Reinforcement Learning with Python – If you are looking for a high-level advanced course on Reinforcement learning, then this is no doubt the best course available in the Udemy platform for you. This is a premium course with a price tag of 29.99 USD, a rating of 4.6 stars, entertaining more than 32,000 students across the world. It is not just about reinforcement learning at the foundation level, but also deep reinforcement learning with its practical implementation using Python programming. The practical implementations of deep learning agents, Q-learning algorithms, deep neural networks, RBF networks, convolutional neural networks with deep Q-learning are the prime grabs of this course.

8. Practical Reinforcement Learning – Another popular course offered by Coursera, best for those looking for practical knowledge of reinforcement learning. It has a total rating of 4.2 stars with more than 37,000 students already enrolled.

What are you waiting for? Start learning!

Hopefully, these resources will help you get a deep understanding of reinforcement learning, and its practical applications in the real world.

RL is a fascinating part of machine learning, and it’s worth spending your time on it to master it. Good luck!


READ NEXT

10 Real-Life Applications of Reinforcement Learning

10 mins read | Author Derrick Mwiti | Updated May 25th, 2021

In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones. In doing so, the agent tries to minimize wrong moves and maximize the right ones. 

In this article, we’ll look at some of the real-world applications of reinforcement learning.

Applications in self-driving cars

Various papers have proposed Deep Reinforcement Learning for autonomous driving. In self-driving cars, there are various aspects to consider, such as speed limits at various places, drivable zones, avoiding collisions — just to mention a few. 

Some of the autonomous driving tasks where reinforcement learning could be applied include trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways. 

For example, parking can be achieved by learning automatic parking policies. Lane changing can be achieved using Q-Learning while overtaking can be implemented by learning an overtaking policy while avoiding collision and maintaining a steady speed thereafter.

AWS DeepRacer is an autonomous racing car that has been designed to test out RL in a physical track. It uses cameras to visualize the runway and a reinforcement learning model to control the throttle and direction.

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