The Best Reinforcement Learning online courses and tutorials for beginners to learn Reinforcement Learning in 2024.

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 recent 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, a 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 a highly curated list of courses that can help you learn reinforcement learning easily.

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Top Reinforcement Learning Courses, Tutorials, Certifications list

Artificial Intelligence: Reinforcement Learning in Python
Reinforcement Learning
Advanced AI: Deep Reinforcement Learning with Python
Reinforcement Learning Foundations Online Class
Deep Reinforcement Learning 2.0
Reinforcement Learning in Finance
Cutting-Edge AI: Deep Reinforcement Learning in Python
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: 4.7 out of 5.0 (9,347 Ratings total)
Duration: 13 Hours
Certificate: Certificate of completion

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 another 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 the 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 (2,863 Ratings total)
Duration: 100 Hours
Certificate: Certificate of completion

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 and supervised and unsupervised learning.

This course offers you;

Introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world and the 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 (3,593 Ratings total)
Duration: 10.5 Hours
Certificate: Certificate of completion

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. Reinforcement Learning Foundations Online Class

Learn the basics of reinforcement learning (RL), including the terminology, the kinds of problems you can solve with RL, and the different methods for solving those problems.

Course rating: 5,968 total enrollments
Duration: 45 m
Certificate: Certificate on completion

The course includes:

Getting Started with Reinforcement Learning
Reinforcement Learning Algorithms
Monte Carlo Method
Temporal Difference Methods
Modified Forms of Reinforcement

In this Reinforcement Learning course, you will learn the basics of this relatively new, but valuable skill. You will get to know 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.

Plus, it shows you how to define and represent reinforcement learning problems. It also delves into RL algorithms, including the Monte Carlo and temporal difference methods. Plus, the course explores deep and multi-agent RL, as well as how inverse learning works and how it can help agents learn by imitation.

You can take the Reinforcement Learning Foundations Online Class certification course on Linkedin Learning.

5. Deep Reinforcement Learning 2.0

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

Course rating: 4.4 out of 5.0 (550 Ratings total)
Duration: 9.5 Hours
Certificate: Certificate of completion

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 overperforming 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 developing use cases for applications of RL for options.

Course rating: 3.5 out of 5.0 (100 Ratings total)
Duration: 17 Hours
Certificate: Certificate of completion

In this course, you will :

This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and developing 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 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 are obtained using reinforcement learning as the course project.

You can take the 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: 4.7 out of 5.0 (559 Ratings total)
Duration: 8.5 Hours
Certificate: Certificate of completion

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.

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 the future, and learning Reinforcement Learning, which is a Machine Learning based paradigm is very important if you looking forward to building a career in the IT industry as a software engineer or data scientist.

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