10 Best Deep Reinforcement Learning Courses You Must Know

Are you looking for Best Deep Reinforcement Learning Courses?… If yes, this article is for you. In this article, you will find the 10 Best Deep Reinforcement Learning Courses. So give your few minutes and find out the Best Deep Reinforcement Learning Courses for you.

Now without any further ado, let’s get started-

Best Deep Reinforcement Learning Courses

1. Become a Deep Reinforcement Learning Expert– Udacity

Rating- 4.6/5

Time to Complete- 4 months (If you spend 10 hours per week)

This is an advanced Nanodegree Program. In this program, you will learn the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods.

You will apply deep learning architectures to reinforcement learning tasks. And train your own agent that navigates a virtual world from sensory data.

Then you will learn the theory behind evolutionary algorithms and policy-gradient methods. And design your own algorithm to train a simulated robotic arm to reach target locations.

At the end of this Nanodegree program, you will learn how to apply reinforcement learning methods to applications that involve multiple, interacting agents.

There are 4 courses in this program.

Courses Include-

1. Foundations of Reinforcement Learning
2. Value-Based Methods
3. Policy-Based Methods
4. Multi-Agent Reinforcement Learning

Extra Benefits-

• You will get a chance to work on Real-world projects.
• You will get Technical mentor support.
• Along with that, you will get Resume services, Github review, and LinkedIn profile review.

Who Should Enroll?

• Those who have Intermediate to advanced Python experience and intermediate statistics and machine learning knowledge.

Interested to Enroll?

If yes, then check it out here– Become a Deep Reinforcement Learning Expert

2. Reinforcement Learning– Udacity

Time to Complete- 4 Months

This is an advanced-level FREE deep learning course on Udacity. This course is good for you if you have intermediate-level machine learning knowledge and you want to engage with the theoretical perspective of machine learning.

In this course, you will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience.

At the end of the course, you will replicate a result from a published paper in reinforcement learning.

In this course, you will get a chance to learn from two of the foremost experts in this field of research, Profs. Charles Isbell and Michael Littman. But before taking this course, you should know Java programming and you are familiar with machine learning algorithms.

Interested to Enroll?

If yes, then check it out here– Reinforcement Learning

3. Deep Learning and Reinforcement Learning– Coursera

Rating- 4.7/5

Time to Complete- 14 hours

In this course, you will learn about Deep Learning and Reinforcement Learning. First, you will learn the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. After that, the course will cover Reinforcement Learning.

Throughout this course, you will also learn Recursive Neural Networks (RNNs), Long-Short Term Memory Networks (LSTM), and Deep Learning with Autoencoders.

Extra Benefits-

• You will get a Shareable Certificate.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have familiarity with programming on a Python and fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.

Interested to Enroll?

If yes, then check it out here– Deep Learning and Reinforcement Learning

4. Reinforcement Learning beginner to master — AI in Python– Udemy

Rating-4.8/5

Time to Complete- 10.5 hours

In this course, you will learn the basics of Reinforcement Learning and implement from scratch adaptive algorithms that solve control tasks based on experience.

This course will also teach you how to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning.

You will understand the different approaches to solving a task using Reinforcement Learning and choose the most fitting and also understand the process of solving a cognitive task using Reinforcement Learning.

Extra Benefits-

• You will get a Certificate of Completion.
• You will also get 19 articles and 1 downloadable resource.
• Along with that, you will get lifetime access to the course material.

Who Should Enroll?

• Those who are comfortable programming in Python and know basic linear algebra, calculus, statistics, and probability theory.

Interested to Enroll?

If yes, then check it out here– Reinforcement Learning beginner to master — AI in Python

5. AWS Machine Learning Foundations Course– Udacity

Time to Complete- 2 months

This is a completely FREE course to learn the fundamentals of advanced machine learning areas such as computer vision, reinforcement learning, and generative AI.

You will get hands-on with machine learning using AWS AI Devices (i.e. AWS DeepLens, AWS DeepRacer, and AWS DeepComposer).

You will learn how to prepare, build, train, and deploy high-quality machine learning (ML) models quickly with Amazon SageMaker and learn object-oriented programming best practices.

Interested to Enroll?

If yes, then check it out here–AWS Machine Learning Foundations Course

6. Tensorflow 2.0: Deep Learning and Artificial Intelligence– Udemy

Rating- 4.7/5

Time to Complete- 21 hours

This course is for beginner-level students. This course starts with some very basic machine learning models and advances to the state of the art concepts. After that, you will learn deep learning concepts, such as Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.

This course includes the following projects-

• Natural Language Processing (NLP)
• Recommender Systems
• Transfer Learning for Computer Vision
• Generative Adversarial Networks (GANs)
• Deep Reinforcement Learning Stock Trading Bot

Extra Benefits-

• You will get a Certificate of Completion.
• Along with that, you will get lifetime access to the course material.

Who Should Enroll?

• Those who are beginners and want to learn about deep learning and AI in Tensorflow 2.0.

Interested to Enroll?

If yes, then check out all details here-Tensorflow 2.0: Deep Learning and Artificial Intelligence.

7. Machine Learning for Trading Specialization– Coursera

Rating- 3.9/5

Time to Complete- 3 months ( If you spend 4 hours per week)

This is a specialization program offered by Coursera. This specialization will teach you how to construct effective trading strategies using Machine Learning (ML) and Python.

After completing this specialization program, you will know how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies.

This specialization program has 3 courses-

1. Introduction to Trading, Machine Learning & GCP
2. Using Machine Learning in Trading and Finance
3. Reinforcement Learning for Trading Strategies

What You’ll Learn-

• Basic concepts of Trading, Machine Learning, and Google Cloud Platform.
• Reinforcement learning basics
• Reinforcement Learning Trading Algorithm Optimization.
• Trading
• Reinforcement Learning Trading Strategy Development
• Reinforcement Learning Trading Algo Development
• Finance
• Investment

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have basic knowledge in Python programming and familiarity with the Scikit Learn, Statsmodels, and Pandas library.
• And those who have a background in statistics and foundational knowledge of financial markets.

Interested to Enroll?

If yes, then check it out here– Machine Learning for Trading Specialization

8. AWS DeepRacer– Udacity

Time to Complete- 2 weeks

This is another completely FREE course to create, train, and fine-tune reinforcement learning models in the AWS DeepRacer 3D racing simulator.

You will also utilize the car’s tech specs, assembly, and calibration to train and deploy your racing model using AWS in both simulated and real-world tracks.

Interested to Enroll?

If yes, then check it out here– AWS DeepRacer

9. Practical AI with Python and Reinforcement Learning– Udemy

Rating- 4.8/5

Time to Complete- 26.5 hours

In this course, you will create your own deep reinforcement learning agents in your own environments. This course focuses on a practical approach with the right balance of theory and intuition with useable code.

You will also learn how Deep Learning with Keras and TensorFlow works, before diving into Reinforcement Learning concepts, such as Q-Learning. After that, this course will walk you through Deep Reinforcement Learning agents, such as Deep Q-Networks.

Extra Benefits-

• You will get a Certificate of Completion.
• You will also get 6 articles and 9 downloadable resources.
• Along with that, you will get lifetime access to the course material.

Who Should Enroll?

• Those who are comfortable with basic Python and installing Python libraries.

Interested to Enroll?

If yes, then check it out here– Practical AI with Python and Reinforcement Learning

10. Deep Reinforcement Learning 2.0– Udemy

Rating- 4.5/5

Time to Complete- 9.5 hours

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.

First, you will learn the fundamentals of Artificial Intelligence such as Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic, and more. After that, you will study in-depth the whole theory behind the model.

In the end, you will implement the model from scratch, step by step, and through interactive sessions.

Extra Benefits-

• You will get a Certificate of Completion.
• You will also get 7 articles and 1 downloadable resource.
• Along with that, you will get lifetime access to the course material.

Who Should Enroll?

• Those who know some maths basics and a bit of programming knowledge.

Interested to Enroll?

If yes, then check it out here– Deep Reinforcement Learning 2.0

And here the list ends. I hope these Best Deep Reinforcement Learning Courses will help you. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

Conclusion

In this article, I tried to cover the 10 Best Deep Reinforcement Learning Courses. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

NOTE- Some of the links in the post are Affiliate Links. This means if you click on the link and purchase the course, I will receive an affiliate commission at no extra cost to you😊.