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Reinforcement Learning

A crash course in this key technique for machine learning.

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.

Interactive
quizzes

6

Concepts and
exercises

50+
  1. 1

    Introduction

    1. Introduction

  2. 2

    Foundations

    1. Included with
      Brilliant Premium

      Value Functions

    2. Included with
      Brilliant Premium

      Dynamic Programming

    3. Included with
      Brilliant Premium

      Monte Carlo

  3. 3

    Extensions

    1. Included with
      Brilliant Premium

      Temporal Difference Learning

    2. Included with
      Brilliant Premium

      Policy Gradient Methods