The Best Reinforcement Learning online courses and tutorials for beginner to learn Reinforcement Learning in 2020.

Disclosure: Coursesity is supported by the learners community. We may earn an affiliate commission when you make a purchase via links on Coursesity.

Reinforcement Learning is a sub-field of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. Reinforcement Learning, also called as RL, is one of the three Machine Learning paradigms apart from Supervised and Unsupervised Learning.

Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks). While both of these have been around for quite some time, it’s only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning. With the  advancement in technology, machine learning has become an indispensable part of the IT industry, thus, possessing, at least, the rudimentary knowledge of the concepts of ML and AI is almost a prerequisite for aspiring software engineers and data scientists. Thus, we bring to you highly curated list of courses which can help you learn reinforcement learning easily.

Top Reinforcement Learning Courses, Tutorials, Certifications list

  1. Artificial Intelligence: Reinforcement Learning in Python

  2. Reinforcement Learning

  3. Advanced AI: Deep Reinforcement Learning with Python

  4. Practical Reinforcement Learning

  5. Deep Reinforcement Learning 2.0

  6. Reinforcement Learning in Finance

  7. Cutting-Edge AI: Deep Reinforcement Learning in Python

  8. Reinforcement Learning

1. Artificial Intelligence: Reinforcement Learning in Python

Complete guide to Artificial Intelligence, prep for Deep Reinforcement Learning with Stock Trading Applications

Course rating: 4.5 out of 5.0 ( 5,828 Ratings total)

In this course, you will :

  • Apply gradient-based supervised machine learning methods to reinforcement learning.
  • Understand reinforcement learning on a technical level.
  • Understand the relationship between reinforcement learning and psychology.
  • Implement 17 different reinforcement learning algorithms.
  • The multi-armed bandit problem and the explore-exploit dilemma
  • Ways to calculate means and moving averages and their relationship to stochastic gradient descent and Markov Decision Processes (MDPs)
  • Dynamic Programming and Monte Carlo
  • Temporal Difference (TD) Learning (Q-Learning and SARSA)
  • Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)
  • Project: Apply Q-Learning to build a stock trading bot

You can take Artificial Intelligence: Reinforcement Learning in Python Certificate Course on Udemy .

2. Reinforcement Learning

Learn Reinforcement Learning from University of Alberta, Alberta Machine Intelligence Institute. The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).

Course rating: 4.8 out of 5.0 ( 576 Ratings total)

In this course, you will :

  • Build a Reinforcement Learning system for sequential decision making.
  • Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more).
  • Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution..
  • Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning.

This course offer you;

  • Introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world and fundamentals of Reinforcement Learning.
  • Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff  
  • Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL.

After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization. You will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment-learning from the agent’s own experience.

You can take Reinforcement Learning Certificate Course on Coursera .

3. Advanced AI: Deep Reinforcement Learning with Python

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks

Course rating: 4.6 out of 5.0 ( 2,627 Ratings total)

In this course, you will :

  • Build various deep learning agents (including DQN and A3C).
  • Apply a variety of advanced reinforcement learning algorithms to any problem.
  • Q-Learning with Deep Neural Networks.
  • Policy Gradient Methods with Neural Networks.
  • Reinforcement Learning with RBF Networks.
  • Use Convolutional Neural Networks with Deep Q-Learning.

This course is all about the application of deep learning and neural networks to reinforcement learning.

In this course, you will build upon what you did in the last course by working with more complex environments, specifically, those provided by the OpenAI Gym:

  • CartPole
  • Mountain Car
  • Atari games

You can take Advanced AI: Deep Reinforcement Learning with Python Certificate Course on Udemy .

4. Practical Reinforcement Learning

Learn Practical Reinforcement Learning from National Research University Higher School of Economics. Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning,etc.

Course rating: 4.2 out of 5.0 ( 297 Ratings total)

In this course, you will:

  • Foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.
  • Teaching your neural network to play games. You will also use it for seq-2-seq and contextual bandits.
  • You will also learn one simple algorithm that can solve reinforcement learning problems with embarrassing efficiency. You will consider the reinforcement learning formalism in a more rigorous, mathematical way.
  • You will learn how to effectively compute the return your agent gets for a particular action - and how to pick best actions based on that return.
  • You will find out how to apply last week's ideas to the real world problems: ones where you don't have a perfect model of your environment.
  • You will learn to scale things even farther up by training agents based on neural networks.
  • You will learn how to build better exploration strategies with a focus on contextual bandit setup. You will also learn how to apply reinforcement learning to train structured deep learning models.

You can take Practical Reinforcement Learning Certificate Course on Coursera .

5. Deep Reinforcement Learning 2.0

The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG

Course rating: 4.5 out of 5.0 ( 248 Ratings total)

In this course, you will :

  • Q-Learning and deep Q-Learning.
  • Policy Gradient and Actor Critic.
  • Deep Deterministic Policy Gradient (DDPG).
  • Twin-Delayed DDPG (TD3).
  • The Foundation Techniques of Deep Reinforcement Learning.
  • How to implement a state of the art AI model that is over performing the most challenging virtual applications.

In this course, you will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, you will be able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).

To approach this model the right way, the course is structured in three parts:

  • Part 1: Fundamentals
  • Part 2: The Twin-Delayed DDPG Theory
  • Part 3: The Twin-Delayed DDPG Implementation

You can take Deep Reinforcement Learning 2.0 Certificate Course on Udemy .

6. Reinforcement Learning in Finance

Learn Reinforcement Learning in Finance from New York University Tandon School of Engineering. This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option.

Course rating: 3.6 out of 5.0 ( 75 Ratings total)

In this course, you will :

  • This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management.
  • By the end of this course, students will be able to use reinforcement learning to solve classical problems of finance such as portfolio optimization, optimal trading, and option pricing and risk management.
  • Practice on valuable examples such as famous Q-learning using financial problems.
  • Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project.

You can take Reinforcement Learning in Finance Certificate Course on Coursera .

7. Cutting-Edge AI: Deep Reinforcement Learning in Python

Apply deep learning to artificial intelligence and reinforcement learning using evolution strategies, A2C, and DDPG

Course rating: 4.8 out of 5.0 ( 203 Ratings total)

In this course, you will :

  • Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines).
  • Understand and implement Evolution Strategies (ES) for AI.
  • Understand and implement DDPG (Deep Deterministic Policy Gradient).

This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies.

You can take Cutting-Edge AI: Deep Reinforcement Learning in Python Certificate Course on Udemy .

8. Reinforcement Learning

Study machine learning at a deeper level and become a participant in the reinforcement learning research community.

Course Level : Best suited for Advanced learners

In this course you will:

  • Explore automated decision-making from a computer-science perspective.
  • Examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience.
  • Replicate a result from a published paper in reinforcement learning.

This course will prepare you to participate in the reinforcement learning research community.

You can take Reinforcement Learning Certificate Course on Udacity.

Summing Up

Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level.

Also, we have seen real-world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation. Thus, we can conclude that Machine Learning and Artificial Intelligence are the technologies of future and learning Reinforcement Learning, which is a Machine Learning based paradigm is very important if you looking forward to build a career in the IT industry as a software engineer or data scientist.


Hey! If you have made it this far then certainly you are willing to learn more and here at Coursesity, it is our duty to enlighten people with knowledge on topics they are willing to learn. Here are some more topics that we think will be interesting for you!