Reinforcement Learning

Reinforcement Learning


Reinforcement Learning (RL) is an area of machine learning concerned with agents (algorithms) take actions in an environment in order to maximize some notion of cumulative reward. RL is the one of three machine learning paradigms, alongside supervised learning and unsupervised learning. In this course, students will learn about RL formulation and get hands-on experience on developing/coding RL algorithms.


Have you ever wondered how computers beat humans in games? Have you ever wondered how advertisements are placed on websites in an optimal fashion? Are you tired of the shortcomings of Supervised and Unsupervised Learning? If you answered yes to any of these questions, this course is for you. In this course, you will learn what Reinforcement Learning is, how to formulate such a problem and how to implement it.


2 Days


Upon completion, students should be able to:

  • Formulate a Reinforcement Learning problem and its associated vocabulary
  • Understand the difference between Supervised, Unsupervised, and Reinforcement Learning
  • Understand Markov Decision Processes (MDPs)
  • Implement Model-based RL, Policy Iteration, and Value Iteration
  • Implement Model-Free Prediction and Control algorithms


  • This course is ideal for data scientists that are interested to learn about next-level algorithms


Prerequisite Knowledge:

  • Familiarity with Python is required
  • Understanding of Probability Theory and Linear Algebra is required
  • Experience with Numpy and Pandas is required
  • Familiarity with Machine Learning concepts is helpful

Prerequisite Courses:


Software & Hardware Requirements

  • Web Browser: Chrome
  • An Internet Connection
  • GoToTraining (for remote classes only)
    Please see the GoToMeeting System Check
  • A computer, laptop, or tablet with a keyboard

Additional Notes

  • The appropriate, web-based programming environment will be provided to students
  • This class is taught in Python only


  • Types of Machine Learning problems, Reinforcement Learning problem, agent, environment, RL vocabulary, and RL shortcomings
  • OpenAI gym and running your first RL code
  • Agent-environment interactions, Markov Processes, Markov Reward Processes, and Markov Decision Processes
  • Policy evaluation, policy iteration, and value iteration
  • Monte-Carlo learning for prediction task, and temporal-difference learning
  • On-policy MC control, on-policy TD learning, and off-policy learning

Upcoming Classes

No classes have been scheduled, but you can always Request a Class.