Udemy – Mastering Deep Q-Learning with GYM-Cliff Walking Environment 2024-6

Published on: 2024-08-12 20:35:36

Categories: 28

Description

Mastering Deep Q-Learning course with GYM-Cliff Walking Environment. This course introduces you to the exciting world of Deep Q-Learning. By using the “GYM-CliffWalking” simulated environment, you will practically familiarize yourself with this powerful reinforcement learning technique and acquire the necessary skills to build intelligent agents. From basic concepts to advanced applications, this course puts you on the path to mastering Q deep learning.

What will you learn in this course?

Understanding the Bellman Equation as the basis of intelligent decision making
Mastery of essential tools “gym” and “deque” for implementing Q deep learning algorithms
Combining deep learning techniques with Q-Learning to improve agent performance
Practical experience in the challenging environment of “GYM-CliffWalking” to optimize agent behavior
Developing intelligent decision making strategies in dynamic and complex scenarios
Explore real examples and case studies to understand the applications of Q deep learning
Learn best practices for efficient algorithm implementation and performance optimization
Building intelligent agents capable of solving problems and adapting to changing environments
Fixing problems and improving the performance of the agent

Who is this course for?

This course is suitable for people of various experience levels, from machine learning enthusiasts to data scientists, artificial intelligence researchers, developers, students, and robotics and automated systems professionals. If you are interested in artificial intelligence and want to gain a deep understanding of Q deep learning and its practical applications, this course is for you.

مشخصات دوره Mastering Deep Q-Learning with GYM-Cliff Walking Environment

Publisher: Udemy
Lecturer: Abdurrahman TEKIN
Training level: beginner to advanced
Training duration: 3 hours and 19 minutes
Number of courses: 12

Course headings

Course prerequisites

Basic Programming Skills: Familiarity with a programming language such as Python will be beneficial for understanding and implementing the algorithms.
Understanding of Machine Learning Concepts: A basic understanding of machine learning concepts, such as supervised and unsupervised learning, will provide a strong foundation for grasping the principles of Deep Q-Learning.