Course details

Advanced Reinforcement Learning: Principles

Advanced Reinforcement Learning: Principles


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This 11-video course delves into machine learning reinforcement learning concepts, including terms used to formulate problems and workflows, prominent use cases and implementation examples, and algorithms. Learners begin the course by examining what reinforcement learning is and the terms used to formulate reinforcement learning problems. Next, look at the differences between machine learning and reinforcement learning by using supervised and unsupervised learning. Explore the capabilities of reinforcement learning, by looking at use cases and implementation examples. Then learners will examine reinforcement learning workflow and reinforcement learning terms; reinforcement learning algorithms and their features; and the Markov Decision Process, its variants, and the steps involved in the algorithm. Take a look at the Markov Reward Process, focusing on value functions for implementing the Markov Reward Process, and also the capabilities of the Markov Decision Process toolbox and the algorithms that are implemented within it. The concluding exercise involves recalling reinforcement learning terms, describing implementation approaches, and listing the Markov Decision Process algorithms.



Expected Duration (hours)
1.2

Lesson Objectives

Advanced Reinforcement Learning: Principles

  • discover the key concepts covered in this course
  • define reinforcement learning and the important terms that are used to formulate reinforcement learning problems
  • differentiate between the implementations of reinforcement and machine learning using supervised and unsupervised learning
  • describe the capabilities of reinforcement learning, illustrating its uses cases and example implementations
  • recognize reinforcement learning terms that are used in building reinforcement learning workflows
  • describe approaches of implementing reinforcement learning
  • describe reinforcement learning algorithms and their features
  • describe Markov Decision Process, its variants, and the steps involved the algorithm
  • describe Markov Reward Process, with focus on value functions for implementing Markov reward process
  • recognize the capabilities of the Markov Decision Process toolbox and the algorithms that are implemented within it
  • recall the reinforcement learning terms, describe reinforcement learning implementation approaches, and list the Markov Decision Process algorithms
  • Course Number:
    it_mlarlndj_01_enus

    Expertise Level
    Intermediate