15+ Best Reinforcement Learning Courses Online in 2023

Do you need to master reinforcement learning as an ML expert?

Reinforcement learning is a major component of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. So becoming a reinforcement learning expert will make you better at developing machine learning applications.

In fact, by taking reinforcement learning courses online, you’ll learn to be able to provide your learning agent with a reward function.

It will be able to figure out the best actions that trigger the best rewards, by itself, so that your intelligent agent can take actions in any environment while maximizing the notion of cumulative reward.

In this article, we are going to look at the best reinforcement learning courses and certifications online to get you started.

These reinforcement learning courses online will teach you advanced deep learning in Python, how to apply reinforcement learning finance as well as Actor-Critic algorithms.

Let’s get started.

1. Reinforcement Learning Specialization | Coursera 

If you are looking for an all in one training on reinforcement learning then you’ve come to the right place.

In this reinforcement learning specialization on Coursera, you’ll find 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).

Through this reinforcement learning training, you’ll be able to harness the full potential of artificial intelligence that requires adaptive learning systems. 

So you’ll know how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end.

It is the best reinforcement learning course online that will help you understand the foundations of modern probabilistic artificial intelligence (AI), as well as prepare you to take more advanced courses or to apply AI tools and ideas to real-world problems. 

Interestingly, you can also apply the skills that you’ll learn in this reinforcement learning specialization to game development, customer interactions, and industrial control systems.

2. Fundamentals of Reinforcement Learning | Coursera 

While reinforcement learning is a subfield of machine learning, it is also a general purpose formalism for automated decision-making and AI. 

This reinforcement learning course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. 

Through this course, you’ll get to understand the importance and challenges of learning agents that make decisions, which is a skill of vital importance today. Moreso because an increasing number of companies are interested in interactive agents and intelligent decision-making. 

Some of the other skills that you’ll learn in this top reinforcement learning course online include how to formalize problems as Markov decision processes, as well as how to implement dynamic programming as an efficient solution approach to an industrial control problem.

By the time you finish this course, you’ll be able to start using RL for real problems, where you have or can specify the MDP. 

3. Artificial Intelligence: Reinforcement Learning in Python | Udemy 

Oftentimes when people mention artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.

It’s the case more so because these tasks are pretty trivial compared to what most people think of AI applications doing, like playing chess and self driving cars.

However, as you’ll discover through this reinforcement learning course on Udemy, reinforcement learning opens up a whole new world. It’s led to new and amazing insights both in behavioral psychology and neuroscience. 

This course will teach you the various analogous processes involved in teaching an agent, teaching an animal or even a human. It’s the closest thing we have so far to a true artificial intelligence. 

Your main project will be applying Q-Learning to build a stock trading bot.

So if you’re ready to take on a brand new challenge and learn about AI techniques then this is the reinforcement learning training for you.

4. Cutting-Edge AI: Deep Reinforcement Learning in Python | Udemy

If you didn’t know, deep reinforcement learning is simply the combination of both reinforcement learning and deep learning.

In fact, it is the maturation of deep learning that has propelled advances in reinforcement learning.

This is one of the best reinforcement learning courses online for picking up skills in the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies.

There are two interesting environments that you’ll cover in this course.

First, the instructor takes you through the classic Atari environments. These are important because they show that reinforcement learning agents can learn based on images alone.

Then there is MuJoCo, which is a physics simulator. It is actually the first step to building a real robot that can navigate the real-world and understand physics.

Finally, you’ll look at Flappy Bird. It used to be everyone’s favorite mobile game just a few years ago.

5. Reinforcement Learning Foundations Online Class | LinkedIn Learning

Reinforcement learning involves the training of machines to accurately learn from their environments, just like a human would.

In fact, it is what has made possible serious innovations in finance, health, robotics, and a variety of other sectors of the economy that have directly impacted mankind.

It is also for this reason that today, a huge number of top tech companies are investing heavily in this field. 

In this reinforcement learning course on LinkedIn Learning, the instructor, Khaulat Abdulhakeem, teaches you the basics of this relatively new, but valuable skill. 

It is one of the best courses for learning the key terminology used in RL, how RL plays a major role in the advancement of AI, and the kinds of problems you can use RL to solve. 

You’ll learn about RL algorithms, including the Monte Carlo and temporal difference methods, deep and multi-agent RL, as well as how inverse learning works.