Intro to Q-learning in RL


Resource | v1 | created by janarez |
Type Interactive
Created 2021-04-30
Identifier unavailable

Description

In this tutorial, we aim to provide readers with a high-level overview of the fundamentals of RL as well as example code in Python, introducing the OpenAI Gym library. We begin with building intuitions about what is considered an RL problem and we introduce formal definitions as well as key terminologies that are used to describe and model an RL application. In parallel, we will focus on solving a concrete example of an RL problem (CartPole) using a classic RL algorithm called Q-learning.

Relations

about Q-learning

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a part...


Edit details Edit relations Attach new author Attach new topic Attach new resource
10.0 /10
useless alright awesome
from 1 review
Write comment Rate resource Tip: Rating is anonymous unless you also write a comment.
Resource level 1.0 /10
beginner intermediate advanced
Resource clarity 10.0 /10
hardly clear sometimes unclear perfectly clear
Reviewer's background 4.0 /10
none basics intermediate advanced expert
Comments 1
janarez
0 0

10 rating 1 level 10 clarity 4 user's background

Clearly explained with a simple example. Jupyter notebook, so you can run the experiment yourself in the browser.