• Online, Self-Paced
Course Description

Some problems are too complicated to describe to a computer and to solve with traditional algorithms, which is why reinforcement learning is useful. In this course, you will learn the fundamentals of reinforcement learning.

Learning Objectives

Introducing Reinforcement Learning

  • start the course
  • describe reinforcement learning and list some of the techniques that agents can use to learn
  • describe additive rewards and discounted rewards
  • describe passive learning
  • describe how to use direct utility estimation for passive learning and how to define the Bellman Equation in the context of reinforced learning
  • describe temporal difference learning and contrast it with direct utility estimation
  • describe active learning and contrast it with passive learning
  • describe exploration and exploitation in the context of active reinforced learning and describe some of the exploration policies used in learning algorithms

Q-learning Algorithm

  • define Q-learning for reinforced learning
  • describe the different parts used in Q-learning and how these can be implemented
  • describe on-policy and off-policy learning and the difference between the two
  • describe why lookup tables aren't ideal for most reinforced learning tasks and how to build some function approximations that can make these problems possible
  • describe how deep neural networks can be used to approximate q-value for given states in Q-learning

Practice: Q-learning

  • describe Q-learning and how to set up the algorithm for a particular problem

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.