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Reinforcement Learning


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/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.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

Prerequisites
None

Expected Duration (hours)
0.6

Lesson Objectives

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
  • 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
  • describe Q-learning and how to set up the algorithm for a particular problem
  • Course Number:
    sd_exai_a07_it_enus

    Expertise Level
    Everyone