Advanced Reinforcement Learning: Principles

Machine Learning    |    Intermediate
  • 11 Videos | 1h 17m 26s
  • Includes Assessment
  • Earns a Badge
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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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 28s
    UP NEXT
  • Playable
    2. 
    Reinforcement Learning Concepts
    4m 32s
  • Locked
    3. 
    Comparing Reinforcement and Machine Learning
    7m 15s
  • Locked
    4. 
    Reinforcement Learning Use Cases
    4m 12s
  • Locked
    5. 
    Reinforcement Learning Terms and Workflow
    8m 58s
  • Locked
    6. 
    Reinforcement Learning Implementation Approaches
    6m 10s
  • Locked
    7. 
    Reinforcement Learning Algorithms
    17m 37s
  • Locked
    8. 
    Markov Decision Process and Its Variants
    10m 27s
  • Locked
    9. 
    Markov Reward Process and Value Functions
    5m 4s
  • Locked
    10. 
    Markov Decision Process Toolbox Capabilities
    5m 41s
  • Locked
    11. 
    Exercise: Reinforcement Learning and MDP Toolbox
    1m 32s

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