Learning Agents: Definition, Components & Examples

Lesson Transcript
Instructor: Beth Hendricks

Beth holds a master's degree in integrated marketing communications, and has worked in journalism and marketing throughout her career.

A learning agent is able to act and adapt based on new information. In this lesson, you'll learn more about learning agents and the four components necessary for their knowledge to expand.

Learning Useful Information

Imagine you've decided to start driving for a cab service. It's a Friday night and you're running your typical route from the nightlife scene to many of the hotels away from the downtown area. You've become accustomed to taking the Maple Street exit to pick up your customers, but tonight you opt for Palace Road for a change of pace. To your surprise, you discover that Palace Road is not only quicker, but you can avoid the accident-prone intersection at Maple and Vine. You decide that, in the future, you'll be sure to take Palace Road.

You probably guessed there's a deeper meaning to the example. It's an illustration of an idea in artificial intelligence (AI) known as a learning agent.

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  • 0:04 Learning Useful Information
  • 0:42 What Are Learning Agents?
  • 1:14 Learning Agent Components
  • 1:46 Application
  • 2:29 Lesson Summary
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What Are Learning Agents?

The cab driver, in the previous example, learned something new by trying something different. Learning agents operate similarly. A learning agent is a tool in AI that is capable of learning from its experiences. It starts with some basic knowledge and is then able to act and adapt autonomously, through learning, to improve its own performance. Unlike intelligent agents that act on information provided by a programmer, learning agents are able to perform tasks, analyze performance, and look for new ways to improve on those tasks - all on their own.

Learning Agent Components

A learning agent is made up of four basic components:

  1. The performance element chooses what action to take; it later shifts to a new action based on feedback and suggestions for improvement

  2. The critic element determines the outcome of the action and gives feedback

  3. The learning element takes the feedback from the critic element and figures out how to make the action better next time

  4. The problem generator is tasked with developing new experiences for the learning agent to try; this is the piece that helps the agent continue to learn

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