Reinforcement Learning: Essentials

Machine Learning
  • 10 Videos | 33m 58s
  • Includes Assessment
  • Earns a Badge
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Explore machine learning reinforcement learning, along with the essential components of reinforcement learning that will assist in the development of critical algorithms for decisionmaking, in this 10-video course. You will examine how to achieve continuous improvement in performance of machines or programs over time, along with key differences between reinforcement learning and machine learning paradigm. Learners will observe how to depict the flow of reinforcement learning by using agent, action, and environment. Next, you will examine different scenarios of state changes and transition processes applied in reinforcement learning. Then examine the reward hypothesis, and learn to recognize the role of rewards in reinforcement learning. You will learn that all goals can be described by maximization of the expected cumulative rewards. Continue by learning the essential steps applied by agents in reinforcement learning to make decisions. You will explore the types of reinforcement learning environments, including deterministic, observable, discrete or continuous, and single-agent or multi-agent. Finally, you will learn how to install OpenAI Gym and OpenAl Universe.

WHAT YOU WILL LEARN

  • define reinforcement learning and describe its essential elements
    recognize the key differences between the reinforcement learning and machine learning paradigms
    depict the flow of reinforcement learning using agent, action, and environment
    describe different state change scenarios and transition processes in reinforcement learning
    recognize the role of rewards in reinforcement learning
  • list the essential steps agents take to make decisions in reinforcement learning
    recognize prominent reinforcement learning environment types
    install OpenAI Gym and OpenAI Universe
    list reinforcement learning elements, agents involved in the process and the steps they take, and reinforcement learning environments

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 9s
    UP NEXT
  • Playable
    2. 
    Reinforcement Learning Basics
    3m 38s
  • Locked
    3. 
    Reinforcement Learning and Machine Learning
    2m 41s
  • Locked
    4. 
    Reinforcement Learning Flow
    3m 14s
  • Locked
    5. 
    State Change and Transition Process
    3m 1s
  • Locked
    6. 
    Rewards and Reinforcement Learning
    4m 1s
  • Locked
    7. 
    Agents in Reinforcement Learning
    2m 28s
  • Locked
    8. 
    Types of Reinforcement Learning Environment
    2m 12s
  • Locked
    9. 
    OpenAI
    4m 52s
  • Locked
    10. 
    Exercise: Reinforcement Learning Elements
    1m 43s

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