19 Courses and Certifications

Online Deep Reinforcement Learning Courses & Certifications

Deep Reinforcement Learning courses offer comprehensive training in combining reinforcement learning and deep neural networks. Students learn fundamental concepts of reinforcement learning, explore deep learning basics, and advanced deep reinforcement learning algorithms. They gain hands-on experience with frameworks like TensorFlow and PyTorch, apply deep reinforcement learning to real-world domains, complete practical projects, and address challenges and ethical considerations. These courses aim to learners of various levels and equip them with skills to create intelligent agents for complex decision-making tasks.

What is Deep Reinforcement Learning Courses

Deep Reinforcement Learning courses explore reinforcement learning and deep neural networks, enabling students to create intelligent agents. These courses cover foundational concepts, advanced algorithms, and hands-on coding using frameworks like TensorFlow and PyTorch. These courses provide applications in real-world scenarios and practical projects. Graduates gain the ability to construct and train agents for complex decision-making tasks, driving innovation across diverse domains.

Who can Pursue Deep Reinforcement Learning Courses?

Deep Reinforcement Learning courses are open to students, researchers, machine learning practitioners, entrepreneurs, domain specialists, AI enthusiasts, and career switchers. These courses provide expertise in merging reinforcement learning and deep neural networks, enabling learners to create intelligent agents for various applications and industries. Prior machine learning knowledge is helpful, but introductory materials target diverse backgrounds.

Eligibility Criteria of Deep Reinforcement Learning Courses

Eligibility criteria for Deep Reinforcement Learning courses with certificates can vary depending on the specific institution or platform offering the course. However, in general, here are some common eligibility considerations:

  • Prerequisite Knowledge: Many deep reinforcement learning courses assume a foundational understanding of machine learning, neural networks, and basic mathematics, including concepts like linear algebra, calculus, and probability.
  • Educational Background: Deep reinforcement learning courses are typically open to individuals with diverse educational backgrounds, including computer science, engineering, mathematics, physics, and related fields. Some courses may require a minimum level of education, such as a high school diploma or equivalent.
  • Programming Skills: Proficiency in programming is often required, with courses commonly using languages like Python. Familiarity with programming libraries and frameworks used in deep learning, such as TensorFlow or PyTorch, can be advantageous.
  • Experience Level: Some courses are designed as introductions to the field and do not require extensive prior experience while others might target experienced machine learning professionals seeking to specialise in deep reinforcement learning.

Skills Required for Deep Reinforcement Learning Courses

To succeed in Deep Reinforcement Learning courses, you should possess a set of foundational skills and knowledge. While the specific requirements can vary depending on the course and your prior experience, here are some key skills that are generally beneficial for pursuing these courses:

  • Programming Skills
  • Machine Learning Fundamentals
  • Mathematics and Statistics
  • Neural Networks
  • Reinforcement Learning Basics
  • Algorithmic Thinking
  • Software Development and Debugging
  • Data Analysis
  • Research Skills
  • Critical Thinking and Creativity

What You Will Learn in Deep Reinforcement Learning Courses

In Deep Reinforcement Learning online courses, you can expect to learn a range of topics and skills that enable you to understand, implement, and apply reinforcement learning principles in combination with deep neural networks. While the exact curriculum can vary depending on the course, here is an overview of what you might learn:

  • Reinforcement Learning Fundamentals
  • Deep Learning Basics
  • Deep Reinforcement Learning Algorithms
  • Exploration Strategies
  • Advanced Topics in DRL
  • Applications and Case Studies
  • Practical Implementation
  • Real-World Projects
  • Ethical Considerations and Challenges
  • Research Trends and Future Directions

Popular Deep Reinforcement Learning Courses by Top Providers

Deep reinforcement learning courses and certifications offer comprehensive education and recognition in the field of combining reinforcement learning and deep neural networks.  Several top providers offer deep reinforcement learning courses to help learners gain expertise in this field. Here are some notable providers offering paid or reinforcement learning free courses.

Career Opportunities After Deep Reinforcement Learning Courses

Completing Deep Reinforcement Learning certificate courses can open up a wide range of exciting career opportunities in various industries that leverage artificial intelligence and machine learning technologies. Here are some potential career paths and roles you might pursue after acquiring expertise in deep reinforcement learning:

Salary Trends in Deep Reinforcement Learning Courses

Actual salaries are widely based on factors such as location, industry, level of experience, educational background, and specific job roles. Below are some general insights into the salary trends for professionals with expertise in deep reinforcement learning.

Job Profile

Average Salary Per Annum

AI Data Analyst

Rs. 4,96,461 (Approx.)

Machine Learning Engineer

Rs. 7,72,224 (Approx.)

Data Scientist

Rs. 9,14,425 (Approx.)

Robotics Engineer

Rs. 4,54,945 (Approx.)

AI Developer

Rs. 7,02,561 (Approx.)

*Source: Payscale

Scope of Deep Reinforcement Learning Courses

The scope of deep reinforcement learning training courses is broad and promising, offering opportunities in high-paying careers across industries like AI research, robotics, gaming, finance, and more. Deep reinforcement learning skills enable professionals to innovate, contribute to cutting-edge technology, and address complex challenges. Graduates can pursue diverse roles, engage in interdisciplinary applications, and make positive societal impacts through AI-driven solutions. The dynamic nature of deep reinforcement learning ensures continued learning and global networking within the AI community.

Top Recruiters in Deep Reinforcement Learning Courses

Several top companies and organisations actively recruit professionals with expertise in deep reinforcement learning. Below are some notable recruiters in the field of deep reinforcement learning.

  • Google DeepMind
  • OpenAI
  • Facebook AI Research (FAIR)
  • Amazon Robotics
  • Tesla
  • Microsoft Research
  • Uber AI
  • IBM Research
  • Intel AI Lab
  • Research Universities
  • Startups and Innovative Companies


1. What is Deep Reinforcement Learning?

Ans: It is a subfield of artificial intelligence that combines reinforcement learning techniques with deep neural networks to create intelligent agents capable of learning and making decisions in complex environments.

2. What are the prerequisites for a Deep Reinforcement Learning course?

Ans: Prerequisites vary by course, but a strong understanding of programming, machine learning fundamentals, and neural networks is often beneficial.

3. Do I need prior experience in Reinforcement Learning or Deep Learning?

Ans: While prior experience is helpful, many courses provide introductory material on reinforcement learning and deep learning concepts.

4. What kind of projects will I work on during the course?

Ans: You may work on projects like training DRL agents to play games, navigate environments, control robotic systems, or optimize decision-making processes.

5. What career opportunities can a Deep Reinforcement Learning course lead to?

Ans: These courses can lead to careers as AI researchers, machine learning engineers, data scientists, robotics engineers, algorithmic traders, and more, depending on your specialisation.

Career Category
Job Role



Offered by


Deep Reinforcement Learning Expert

This course by Udacity has been specially crafted for candidates seeking to improvise their machine learning and deep learning skills. Pioneering machine learning algorithms have been focused on along with furnishing course-takers with hands-on coding experience. These exercises are challenging to prepare the candidates for the best and the worst. They are also unrestricted and cover a variety of concepts.

This Deep Reinforcement Learning course online certification deals with unique concepts which make this course stand out. Using a combination of Python and deep learning libraries, the candidates can implement their learnings for good. The projects submitted by students shall form their portfolio, entitling them to lucrative jobs in the field. The course basically explores the budding interest and trends in the deep learning arena so that candidates can strike a niche in their career.

Experienced mentors, scientifically-crafted syllabus, additional resources for improvisation and many more features make this course an ideal fit for exploring the spell-bounding innovations in Artificial Intelligence. The course serves as the perfect foundation for learning the mechanisms concerning gaming, robotics, and financial trading.

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4 Months
Skills Covered:
Knowledge of deep learning
A Complete Reinforcement Learning System Capstone

Offered by

University of Alberta, Edmonton , Alberta Machine Intelligence Institute via Coursera

A Complete Reinforcement Learning System (Capstone)

The Reinforcement Learning System (Capstone) certification course is a part of the ‘Reinforcement Learning Specialisation’ programme available on Coursera. This course is the 4th and final course in the series and aims to provide the students with complete practical knowledge on how to carry out Reinforcement Learning solutions in the real world.

In the Reinforcement Learning System (Capstone) online course, students will gain well-rounded and application-based skills that will help them in implementing Reinforcement Learning. They will complete the entire Reinforcement Learning problem-solving process – starting with the formulation of a problem, assessing the appropriate algorithm for the problem, and validating the impacts of the algorithm.

The Reinforcement Learning System (Capstone) programme by Coursera is suited for anyone who is expected to perform RL solutions in their field, along with the knowledge of skills like Machine Learning and Reinforcement Learning. The candidates will delve deep into the formalisation of a problem and how to translate it into an MDP. Finally, they will submit their Parameter Study towards the end of the course.

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6 Weeks
Skills Covered:
Knowledge of Algorithms Machine learning Knowledge of Artificial Intelligence
Practical Reinforcement Learning

Offered by


Practical Reinforcement Learning

The Practical Reinforcement Learning programme by Coursera is the fourth out of the seven courses included in the ‘Advanced Machine Learning Specialization’. Offered by the National Research University- Higher School of Economics, this online programme will make you an expert in the field of machine learning.

Reinforcement Learning is one of the fundamentals of machine learning. Without getting a good grasp of this particular area, one cannot master the art of artificial intelligence. With the certification course in Practical Reinforcement Learning by Coursera, you can get in-depth knowledge about Reinforcement Learning and hone the various skills required to master this area of machine learning.

The Practical Reinforcement Learning online course by Coursera is well equipped with a planned curriculum as well as offerings like graded quizzes and assignments, peer feedbacks, practice quizzes, etc. that will help you to become proficient in this field. You will earn a shareable certificate upon completion as well.

The Practical Reinforcement Learning online programme by Coursera is a full package that covers all the core concepts of Reinforcement Learning, including free methods, dynamic programming, value-based methods, neural networks, algorithm-writing, model-free methods, and more.

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6 Weeks
Skills Covered:
Programming skills Knowledge of Algorithms Machine learning Data science knowledge Knowledge of Artificial Intelligence

Offered by

Google , New York Institute of Finance, New York via Coursera

Reinforcement Learning for Trading Strategies

An understanding of Reinforcement learning (RL) is crucial in machine learning that looks at how gradient boosting should act in an environment to get the most rewards over time. Reinforcement learning is one of the three basic ways that machines can learn, along with supervised learning and unsupervised learning. The Reinforcement Learning for trading Strategies certification course is designed by the New York Institute of Finance along with Google Cloud and taught by Jack Farmer - specializing in training and consulting solutions, which is presented by Coursera

Reinforcement Learning for Trading Strategies online classes offer 12  hours of digital lessons that are intended to provide students with a comprehensive understanding of reinforcement learning (RL) and the benefits of using RL in trading strategies, as well as how RL has been used with neural networks and review LSTMs. 

With Reinforcement Learning for Trading Strategies online training students will learn more about how they can be used to look at data over time. Aspirants will be able to use reinforcement learning to build trading strategies, recognize the distinction between actor-based policies and value-based policies, and use RL as part of a momentum trading strategy.

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3 Weeks
Skills Covered:
Financial knowledge Machine learning SQL knowledge Knowledge of equities Trading skills

Offered by


Reinforcement Learning

The course of Reinforcement learning offered by Edureka is a well structured programme specially designed for candidates having keen interest in machine learning and are eager to build a career in this field.

This course is a great resource for getting familiar with reinforcement learning, which falls in the area of machine learning. During this course the learners will be introduced to branches and elements of machine learning, bellman equation, value iteration, policy gradient methods, monte carlo methods and value function.

In addition to this they will also get sound knowledge of RL agent taxonomy, bandit algorithms, temporal difference (TD) methods, markov process and dynamic programming. All this knowledge will help them perform better in unfavorable conditions.

This certification and knowledge offered during the programme is of great benefit to all the people who want to explore the space of reinforcement learning, and to working professionals related to computer science fields like web development, programming and software development for getting increment in salary or for getting promotion. The rapidly increasing popularity of machine learning makes this course even more profitable in terms of employment.

...Read More
Skills Covered:
Programming skills Machine learning

Offered by


Deep Reinforcement Learning 2.0


Offered by

Offered by

Offered by


Reinforcement Learning



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