Q-Learning
-
Intro
-
Video Overview
Information
| Primary software used | Jupyter Notebook |
| Course | Q-Learning |
| Primary subject | AI & ML |
| Secondary subject | Machine Learning |
| Level | Intermediate |
| Last updated | November 11, 2024 |
| Keywords |
Responsible
| Teachers | |
| Faculty |
Q-Learning 0/1
Q-Learning
Q-learning is a model-free reinforcement learning algorithm that seeks to learn the optimal action-selection policy by iteratively updating the estimated value, or “Q-value,” of taking a specific action in a given state based on the received reward and the estimated value of future states. It does this by balancing exploration of new actions and exploitation of known rewards, eventually converging to the optimal policy that maximizes cumulative rewards over time.
For this tutorial you need to have installed Python, Jupyter notebooks, and some common libraries including Scikit Learn. Please see the following tutorial for more information.
Download the Jupyter notebook here to follow along with the tutorial.
Q-Learning 1/1
Video Overviewlink copied
Write your feedback.
Write your feedback on "Q-Learning"".
If you're providing a specific feedback to a part of the chapter, mention which part (text, image, or video) that you have specific feedback for."Thank your for your feedback.
Your feedback has been submitted successfully and is now awaiting review. We appreciate your input and will ensure it aligns with our guidelines before it’s published.