Reinforcement learning is an active area of research in machine learning concerning developing different algorithms or models that can select and perform the best actions in a complex environment to maximize cumulative rewards.
During the 2020s, reinforcement learning has become an integral part of technological advancement in many industries. There are numerous applications of the technology, ranging from industrial automation, stock price prediction to self-driving cars.
Hence, reinforcement learning specialists are in demand more than ever. This fact unsurprisingly makes the career extremely lucrative.
According to Payscale, those who master reinforcement learning in the United States earn as high as $123000 per year on average, far above most IT jobs.
Although reinforcement learning is challenging to master, you can learn the fundamental concepts that you can use to solve a real-world problem from quality online courses.
The drawback is that not all online reinforcement learning courses are worth buying. If you are not careful, you may end up enrolling in a cash-grab course that provides no value.
Hence, I decided to give everyone a helping hand. This post will feature this list of the best reinforcement learning courses available to learn online in 2022. Hence, if you are interested in this topic, you can freely choose the one that suits your learning style.
Affiliate Disclosure: This post from Victory Tale contains affiliate links. If you purchase courses through them, we will receive a small commission from their providers.
Nevertheless, as we value integrity and prioritize our audience’s interests, you can rest assured that we will present all the courses truthfully.
Things You Should Know
Reinforcement learning is not for absolute beginners. You will need background knowledge of the following before enrolling in any of the courses.
- Probability & Statistics
- College-level Calculus and Linear Algebra
- Advanced Python programming (NumPy and Pandas included)
- Machine Learning Algorithms (Supervised and Unsupervised Learning)
- Neural Networks and Deep Learning
- Knowledge of deep learning frameworks such as Tensorflow and PyTorch
The following are the criteria for best reinforcement learning courses.
- Credible instructors
- Up-to-date course content
- User-friendly and reliable learning platform
- Quality course materials ( practical exercises, quizzes, real-world projects, etc.)
- Excellent value for money
- Mostly positive student reviews
- My experience with the course and platform (if any) must be positive.
1. Become a Deep Reinforcement Learning Expert
This nano degree program offered by Udacity is unarguably one of the best training programs for reinforcement learning. Besides learning from leading experts, you will work on multiple projects and obtain relevant hands-on experience.
The program consists of four sections as follows:
1. Foundations of Reinforcement Learning – The first section will drill deep into the fundamental concepts of reinforcement learning. You will write your implementation of classical solution methods.
2. Value-Based Methods – The second section will cover the applications of deep learning architectures to provide solutions to reinforcement learning problems and tasks.
Subsequently, you will use neural networks to train an agent to perform specific tasks in the virtual world.
3. Policy-Based Methods – The third section will explain the theories behind evolutionary algorithms and policy gradient methods. Later on, you will build your personalized algorithm to direct a simulated robotic arm to specific locations or even walk!
4. Multi-Agent Reinforcement Learning – Real-world applications can involve numerous interacting agents. Hence, you will learn to apply reinforcement learning methods and techniques to such applications and understand how AI researchers coordinate all the agents.
Subsequently, you will train your agents to play sports.
Apart from assignments and quizzes, you will work on three real-world projects in the course. Each project will have different layers of challenges.
You can choose to complete only a basic project if you have limited time to study. Alternatively, you can work on much more sophisticated tasks that deserve to be shown on your Github portfolio and helps you stand out from other job applicants.
In addition, all Udacity students gain access to three types of student support as follows:
Technical Mentor Support – You can send an email to inquire about any questions related to the course content 24/7. According to Udacity, most students receive a response in less than an hour, which is extremely fast.
Thus, you don’t need to wait for days for an instructor to answer your question.
Project Reviews – This support makes this Udacity program shine above other competitors. You can request unlimited project reviews to receive personalized feedback, recommendations, industry best practices from experts.
The turnaround time is also speedy. You will receive feedback in an hour or so. Unlimited requests and a fast turnaround together would create a healthy feedback loop that significantly assists your learning.
Career Services – Like web development bootcamps, Udacity provides career services to all its students. The team will review your resume, LinkedIn profile, and Github portfolio to ensure a smooth job application process that may land you numerous job interviews.
The estimated time to complete the entire program is four months, in which you should spend up to 15 hours per week. However, you can freely adjust the schedule, as the program is 100% self-paced.
Still, keep in mind that Udacity uses a subscription pricing model. The more you spend time on the course, the higher the tuition.
Currently, this program costs $399 per month. Alternatively, you can choose to pay for four months at once and receive a 15% discount, lowering the tuition to $339 per month on average.
However, Udacity frequently offers financial support and discounts. Both could lower the tuition by up to 75%. Hence, you can enroll in its excellent program by paying only $100 per month or even lower.
Pros and Cons
- Unarguably one of the most comprehensive courses to build your deep reinforcement learning skills.
- Learn from leading experts who were machine learning engineers or researchers at top universities, agencies, and tech giants
- Work on three real-world projects
- Fast and reliable student support
- Unlimited Project Reviews
- Career Services are a plus.
- Udacity frequently updates its course materials. Thus, outdated content is rare.
- 100% self-paced
- Expensive compared to other reinforcement learning courses
- The program requires intense weekly commitments, which may not be optimal for learners with full-time employment. Although you can set your schedule, you will need to pay extra tuition if you need more time.
2. Deep Learning on Azure with Python: Reinforcement Learning
This FutureLearn course from CloudSwyft is another reinforcement course you may want to consider. You will learn how to use reinforcement learning and artificial intelligence to solve real-world problems.
Unlike other courses, you will use Microsoft Azure’s cloud infrastructure in all the tasks.
Below is a summary of the course content in this course.
- Introduction to Reinforcement Learning using Python
- Dynamic Programming Algorithms and Temporal Difference Learning
- Introduction to Project Malmo (a platform for AI experimentation built in Minecraft)
- Multi-arm bandit problem (a classic reinforcement learning problem)
- Policy Gradient Methods
- Actor-Critic RL
According to FutureLearn, you should spend 5 hours per week on the course, and you will finish it in 6 weeks. The workload is hence manageable for all students.
After you complete 90% of the course content, you will receive a digital certificate of completion from FutureLearn.
The tuition of this course is $39 per month. As this course is part of an ExpertTrack, you can take four other courses in the same program (FutureLearn’s ExpertTrack functions similarly to Coursera’s specialization and edX programs) without further charges.
In fact, you can choose to take only this reinforcement learning course without touching others. However, I do not recommend this option.
This is because the knowledge you obtain from taking all the courses would also be sufficient for passing the AI-100/AI-102 exam (Microsoft Azure AI Engineer Associate Exam). Thus, taking all the courses would be highly beneficial for those who plan to pursue the AI engineer certification.
Furthermore, the entire program is accredited by Microsoft. If you enroll in this ExpertTrack program, you will also receive a free voucher to take the exam online. You can then consider this program as a fast pass to this highly regarded certification.
If you are unsure whether this program is the right option, you can start a 7-day trial to try the lessons.
Pros and Cons
- Well-structured curriculum
- Self-paced learning
- Manageable workload
- Accredited by Microsoft + Free exam voucher
- Join a student support community
- Affordable pricing
- 7-day free trial
- The entire program uses Microsoft Azure. Thus it may not be optimal if you want to use other similar cloud infrastructures such as AWS.
- Starting a free trial requires a credit card.
3. Reinforcement Learning Specialization
This Coursera specialization from the University of Alberta provides excellent training on reinforcement learning.
Upon program completion, you will understand how reinforcement learning is related to other branches of machine learning. Furthermore, you will be able to build a reinforcement learning system of your own.
The program comprises four minor courses as follows:
1. Fundamentals of Reinforcement Learning – The first course will introduce you to the foundational concepts of reinforcement learning. You will learn about basic exploration methods, value functions, dynamic programming, and methods to formalize problems as Markov Decision Processes.
2. Sample-Based Learning Methods – The second course will drill deep into algorithms that can learn based on trial and error interaction with the environment.
You will learn about Monte Carlo methods and temporal difference learning methods, such as Q-learning, in detail. Later, you will build algorithms that potentially combine model-based planning (Dyna) and temporal difference updates to speed up the learning process.
3. Prediction and Control with Function Approximation – The third course will equip you with the tools and techniques to solve complex reinforcement learning problems, particularly those with high-dimensional state spaces.
You will first grasp how to extend Monte Carlo and Temporal Difference methods to the function approximation setting. Subsequently, you will learn about feature construction techniques for reinforcement learning through neural networks.
Finally, the course will drill deep into policy gradient methods. You will implement an Actor-Critic method in a discrete state environment.
4. Capstone – This is essentially a huge real-world project, providing students with an opportunity to put together and use the knowledge learned from former courses. You will implement reinforcement learning solutions to a problem and wrap up the program by assessing your RL agents.
Important Note: The course seems to be based on a popular book: “Reinforcement Learning: An Introduction” by Sutton and Barto. Hence, if you have read the book, you may not need this course at all.
The workload for this course is perfectly manageable. You should spend 4 hours per week, and you will finish the program in 5 months.
This full access to the program, which includes graded assignments and a certificate of completion, costs $79 per month. Otherwise, you can audit the program for free.
Pros and Cons
- Learn from professors of a top Canadian University
- Well-structured and comprehensive curriculum
- Self-paced learning
- Good course materials
- Free auditing
- The instructors and TAs are not always responsive on the Q&A Forum.
- Some learners have technical issues with the assignments.
4. Lazy Programmer’s Series
Lazy Programmer Team is a team of highly knowledgeable data scientists and machine learning engineers. The team has created a series of advanced courses on machine learning, deep learning, and related advanced topics.
I have taken some of these courses and appreciated the teaching style. Thus, I decided to recommend them to you. The three courses below (3.1,3.2, and 3.3) are courses from Lazy programmer that you will need to purchase separately.
However, all Lazy Programmer courses are in-depth. Many students fail to understand the content because their background knowledge is insufficient. You will need to make sure you have strong technical skills (see prerequisites above).
4.1) Artificial Intelligence: Reinforcement Learning in Python
This course is a reinforcement learning tutorial, thus best for those who have never taken any course before. Compared to the other two courses, this one will not cover deep learning content. Thus, you will learn core reinforcement learning concepts before proceeding to more advanced applications.
Below is a summary of what you will learn from the course.
- Multi-arm Bandit (Explore-Exploit Dilemma, Optimistic Initial Values Theory, UCB1 Theory, etc.)
- Markov Decision Processes
- Dynamic Programming and Monte Carlo Method
- Temporal Difference Learning and Approximation Methods
- OpenAI Gym
Once you complete all the lessons above, you will work on the project by using Q-learning to build a stock trading bot, gathering hands-on experience in the process. The total length of this video course is 14.5 hours.
The course receives mostly positive reviews. It scores 4.6/5.0 from more than 8700 ratings.
4.2) Advanced AI: Deep Reinforcement Learning in Python
This course is a sequel to the former course. Throughout the lessons, you will apply deep learning and neural networks to reinforcement learning. Hence, your AI will be much more complex and compelling than those created in the 3rd course.
You can choose to use either Tensorflow or Theano to perform tasks in this course. There is no difference in the learning quality between the two.
Below is a summary of all course content.
- Reinforcement Learning Review and OpenAI Gym
- TD Lambda
- Policy Gradients
- Use Deep Q-Learning with CNNs (Convolutional Neural Networks)
- Use A3C to build various deep learning agents
- Apply advanced reinforcement learning algorithms to any real-world problems
The sales page indicates that the course is 10.5 hours long. However, some of its content replicates the third course. The actual length is approximately 6 hours.
The course scores 4.6/5.0 from almost 4000 ratings.
4.3) Cutting-Edge AI: Deep Reinforcement Learning in Python
As the last sequel in the series, the course unsurprisingly covers the most complex reinforcement learning topics. You will learn about cutting-edge AI technology, including A2C and DDPG.
Unlike Course 3.2, you cannot choose between Tensorflow and Theano. You will use Tensorflow in this course.
What you will learn from the course is as follows.
- Reinforcement Learning Review
- Advantage Actor-Critic (A2C)
- Deep Deterministic Policy Gradient (DDPG)
- Evolution Strategies (ES)
The instructor will introduce you to several case studies that help you understand the theories throughout the course. This includes a physics simulator and a mobile game.
This course is the shortest in a 3-course series. The actual length is approximately 5 hours. Currently, the course receives 4.7/5.0 stars from 950+ ratings.
Pros and Cons
Below are the Pros and Cons of Lazy Programmer’s courses.
- Learn practical knowledge from a highly knowledgeable instructor
- In-depth and informative lessons
- Clear explanations
- Good course materials
- Lifetime access + 30-day money-back guarantee
- Inexpensive (You can get the course when on sale at $20 or lower for each)
- You will need to take two other courses to complete all reinforcement learning content.
- The course may be too math-heavy for some.
- All three courses contain duplicate content (i.e., Python Review and Learning Strategies). Thus, the actual length of the course is much lower than the information that the sales pages provided.
5. Deep Reinforcement Learning 2.0
This Udemy course is another promising alternative to learn reinforcement learning. You will learn from Hadelin de Ponteves, a founder of an AI company who has years of experience in the industry.
The instructor aims to cover the fundamental concepts of deep reinforcement learning in this course. Below is a summary of topics that you will learn.
- Summary of all fundamental concepts (Q-Learning, Deep Q-Learning, Actor-Critic, Policy Gradient)
- Artificial Neural Networks
- Q-Learning and Deep Q-Learning
- Twin Delayed DDPG Theory and Implementation
As a 9.5-hour course, Deep Reinforcement Learning 2.0 is a compact version of Lazy Programmer’s three courses. Hence, you should not expect this course to be in-depth. It will only touch the basics. You will need other courses if you want to build practical deep learning agents.
Reviews: 4.6/5.0 from 800+ ratings
Pros and Cons
- Clear and step-by-step explanations of concepts
- Use images and other colorful visuals to help improve your understanding
- Well-structured Curriculum
- Lifetime Access + 30-day money-back guarantee
- Inexpensive ($20 or lower when on sale)
- Not in-depth. You will need other courses to master reinforcement learning
- Meager assignments and exercises
Modern Reinforcement Learning: Actor-Critic Algorithms – This Udemy course by Phil Tabor drills deep into Actor-Critic Algorithms, which could be beneficial if you want to learn more about policy gradients and Actor-Critic Methods, particularly DDPG and TD3.
However, the instructor has not updated the course for almost a year. Thus, I decided not to include this course in the above list.