Best Online Courses for Reinforcement Learning

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Best Online Courses for Reinforcement Learning

As per Wikipedia, Reinforcement learning can be defined as an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement Learning requires a lot of data; therefore, it is most applicable in domains where simulated data is readily available like gameplay, robotics.

Reinforcement Learning is quite widely used in building Artificial Intelligence for playing computer games. AlphaGo Zero is the first computer program to defeat a world champion in the ancient Chinese game of Go. Others include ATARI games, Backgammon, etc. In robotics and industrial automation, reinforcement learning is used to enable the robot to create an efficient adaptive control system for itself that learns from its own experience and behaviour.

 Other applications of Reinforcement Learning include text summarization engines, dialog agents (text, speech) which can learn from user interactions and improve with time, learning optimal treatment policies in healthcare and Reinforcement Learning (RL) based agents for online stock trading.

Below is a list of courses for reinforcement learning available online based on research and a comparison of different courses.

1. Reinforcement Learning Specialization by Coursera

The course is offered by the University of Alberta. The is a self-paced course with a rating of 4.7. This reinforcement learning specialization program offers four different courses that will help you explore the power of adaptive learning systems and artificial intelligence. In this program, you will learn how reinforcement learning solutions can help you solve real-world problems via trial-and-error interaction by implementing a complete Reinforcement learning solution from beginning to end. The program is designed by the experienced faculty of the University of Alberta, so you will be in direct touch with the instructors to resolve your queries. With the completion of the specialization program, you will have a clear understanding of modern probabilistic artificial intelligence. The key features of course include:

  • programming assignments and quizzes, students
  • Build a Reinforcement Learning system that knows how to make automated decisions.
  • Understand how RL relates to and fits under the broader umbrella of machine learning, deep learning, supervised and unsupervised learning. 
  • Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradient, Dyna, and more).  
  • Understand how to formalize your task as a RL problem, and how to begin implementing a solution.
  • The tools learned in this Specialization can be applied to game development (AI), customer interaction, smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more.

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2. Become a Deep Reinforcement Learning Expert by Udacity

This course is designed by expert instructors of Udacity. This nano degree program is self-paced with a rating of 4.5 out of five. This course of the duration of four months will help you learn the deep reinforcement learning skills, which powers the advances in AI. This program requires experience with Python, probability, machine learning, and deep learning. See detailed requirements. The key uses of the course include:

  • Learn the fundamental concepts of reinforcement learning, and how to apply it to earn architectures to reinforcement learning tasks.
  • Understand the theory behind evolutionary algorithms and policy-gradient methods to design your own algorithm for training a simulated robotic arm.
  • Get guidance and support from knowledgeable mentors who are focused on answering your questions, motivating you, and keeping you on track.
  • Avail personal career coaching, interview preparation and resume services, GitHub reviews, and LinkedIn profile review after finishing the course.

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3. Advanced AI: Deep Reinforcement Learning in Python
by Udemy

This course from Udemy of duration 9 hours has a rating of 4.6 out of five. This course will teach you all about the application of deep learning, neural networks to reinforcement learning. This course, you will learn how reinforcement learning is entirely a different kind of machine learning as compared to supervised and unsupervised learning. You will learn how supervised, and unsupervised machine learning algorithms can be used for analysing and making predictions about data, but reinforcement learning can be used to train an agent to interact with an environment and maximize its reward. The key features of the course are:

  • 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
  • The course will offer you 10.5 hours on-demand video
  • Full lifetime access to the resources
  • Access on mobile and TV of the resources
  • Certificate of completion will be rewarded.

The pre-requisites of the course are knowledge of reinforcement learning basics, MDPs, Dynamic Programming, Monte Carlo, TD Learning, college-level math is helpful, and experience building machine learning models in Python and NumPy.

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4. Artificial Intelligence: Reinforcement Learning in Python by Udemy

This course of duration 10 hours with a rating of 4.6 out of 5, will guide you to every aspect of artificial intelligence included with supervised and unsupervised machine learning algorithms. You will learn how the reinforcement learning paradigm is completely different from supervised and unsupervised learning. The instructor of the course, Lazy Programmer, is an experienced artificial engineer who will assist you at every stage of learning. He will help you learn how to create deep learning models to predict click-through rate and user behaviour while providing an overview of different concepts of artificial intelligence. The course covers the multi-armed bandit problem and the explore-exploit dilemma, ways to calculate means and moving averages and their relationship to stochastic gradient descent, Markov Decision Processes (MDPs), Dynamic Programming, 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), How to use OpenAI Gym, with zero code changes, and finally project on applied Q-Learning to build a stock trading bot. The key features of the course are:

  • 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
  • 14.5 hours on-demand video will be provided after enrolling to the course
  • Full lifetime access to the resources of the courses.
  • Access on mobile and TV to the resources of the course.
  • Certificate of completion will be rewarded.

The pre-requisites of the program are knowledge of Calculus, Probability, Object-oriented programming, Python coding, NumPy coding, Linear regression, and Gradient descent.

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5. Practical Reinforcement Learning by Coursera

The course is offered by HSE University which is one of the top research universities in Russia. This is one of the best courses on Reinforcement learning with a practical approach which has a rating of 4.1 out of 5. In this course, you will get be introduced to the foundation of RL methods, such as policy iteration, Q-learning, policy gradient, and many more. This course is offered by the National Research University Higher School of Economics as a part of the Advanced Machine Learning Specialization program. So, on completion of this course, you can enrol yourself in advanced courses of machine learning to expand your knowledge. This course is included with multiple video lectures, practise exams, quizzes, and external resources so that you can analyse your expertise at every stage of learning and expand your skills. The key features of the program are:

  • An introductory as well as a comprehensive course that is designed to provide you with all the knowledge of Reinforcement Learning in machine learning.
  • Learn how to use deep learning neural networks to resolve reinforcement learning problems.
  • Know about the state-of-the-art RL algorithms and how to apply duct tape to them for practical problems.
  • Get continuous support from a team of experts as well as guidance form the instructors for any queries related to the course.
  • Receive a certificate of completion that can be shared
  • The duration of the course is 6 weeks.

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6. Reinforcement Learning by Udacity

Udacity offers a comprehensive free reinforcement learning course that is created by Georgia Tech. This course has a rating of 4.5 out of five.  This course provides a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. 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. The course key features are:

  • The course is free of cost.
  • The level of the course is advanced.
  • The course content is rich, and taught by experienced people of the industry.
  • This is a self-paced course provided with interactive quizzes.

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7. Fundamentals of Reinforcement Learning by Coursera

This a highly rated course offered byUniversity of Alberta and Alberta Machine Intelligence Institute on the platform of Coursera. The instructors for the course are Martha White and Adam White. This course covers Sequential Decision-Making, Markov Decision Processes, Value Functions & Bellman Equations and Dynamic Programming. The key features of the course are:

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8. A Complete Reinforcement Learning System by Coursera

This is a highly recommended course offered in the platform of Coursera by the University of Alberta and Alberta Machine Intelligence Institute. The instructors for this particular course are Martha White and Adam White. This course covers Formalize Word Problem as MDP, Choosing the Right Algorithm, Key Performance Parameters, and much more. The key features of the course include:

  • This a free online course with a paid certificate program.
  • The level of the course is intermediate that implies previous knowledge in the field is a pre-requisite for this particular course.
  • The duration of the course is six weeks.
  • You will be provided with a lot of study material.

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9. Reinforcement Learning in Finance by Coursera

This is a less opted course of reinforcement learning offered by New York University in the platform of Coursera.  This course focuses on the finance applications of reinforcement learning. The course is taught by Igor Halperin. The course covers MDP and Reinforcement Learning, MDP model for option pricing, Dynamic Programming Approach, MDP model for option pricing – Reinforcement Learning approach, and RL and INVERSE RL for Portfolio Stock Trading. The key features of the course include:

  • This a free online course with a paid certificate program.
  • The level of the course is intermediate that implies previous knowledge in the field is a pre-requisite for this particular course.
  • The duration of the course is four weeks.
  • You will be provided with a lot of study material.

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10. Modern Reinforcement Learning: Actor-Critic Algorithms by Udemy

This is a 4.5 rated course offered by Udemy. The course is intermediate to the advanced level program which needs previous knowledge and familiarity in this field. The covers a wide range of topics such as the Bellman equation, Markov decision processes, monte Carlo prediction, Monte Carlo control, temporal difference prediction, temporal difference control with q learning and much more. The key features of the course may include:

  • This is a paid course.
  • 8 hours on-demand video will be provided after enrolling to the course.
  • You will get 59 downloadable resources for this course.
  • Full lifetime access to the resources of the courses.
  • Access on mobile and TV to the resources of the course.
  • Certificate of completion will be rewarded.

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