Udemy, Coursera, 2U/edX Face Lawsuits Over Meta Pixel Use

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Brilliant

Reinforcement Learning

via Brilliant

Go to class Write review

Overview

This course was written by Tessa van der Heiden, a researcher and developer of autonomous driving algorithms at BMW.

In this course, you'll learn the mathematical underpinnings of reinforcement learning, a foundational machine learning technique in which an agent (or algorithm) is trained by trial and error. By rewarding the agent for good outcomes, it "learns" optimal strategies, which can be applied to problems in domains like robotics, quantitative trading, and game theory.
This course is intended for young professionals who are interested in applying machine learning techniques for decision making, or students who are pursuing a machine learning career or preparing for interviews.

Syllabus

  • Introduction:
    • Introduction: How does a computer devise a strategy to play a game optimally?
  • Foundations:
    • Value Functions: When transitioning between various options, the algorithm must quantify how good these options are.
    • Dynamic Programming: Optimize an interconnected system by reducing it into smaller systems.
    • Monte Carlo: If we make random moves a large number of times, we might notice a pattern that allows us to solve the problem deterministically.
  • Extensions:
    • Temporal Difference Learning: Explore a method of reinforcement learning that updates every time step — not just at the end of the episode.
    • Policy Gradient Methods: These methods take a different approach — by learning the optimal policy directly.

Reviews

Start your review of Reinforcement Learning

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.