Practical Reinforcement Learning using Python - 8 AI Agents - Use Cutting-Edge Reinforcement Learning algorithms in Environments like Flappy Bird, Mario, Stocks and Much More!!
- Bestseller
- Created by Samuel Boylan-Sajous
- English [Auto]
Description
Join the most comprehensive Reinforcement Learning course on Udemy and learn how to build Amazing Reinforcement Learning Applications!
Do you want to learn how to build cutting edge trading algorithms that leverage todays technology? Or do you want to learn the tools and skills that are considered the state of the art of Artificial Intelligence? Or do you just want to learn Reinforcement Learning in a Highly practical way?
After completing this course you will be able to:
Build any reinforcement learning algorithm in any environment
Use Reinforcement Learning for your own scientific experiments
Solve problems using Reinforcement Learning
Leverage Cutting Edge Technologies for your own project
Master OpenAI gym's
Why should you choose this course?
This course guides you through a step-by-step process of building state of the art trading algorithms and ensures that you walk away with the practical skills to build any reinforcement learning algorithm idea you have and implement it efficiently.
Here's what's included in the course:
Atari Reinforcement Learning Agent
Build Q-Learning from scratch and implement it in Autonomous Taxi Environment
Build Deep Q-Learning from scratch and implement it in Flappy Bird
Build Deep Q-Learning from scratch and implement it in Mario
Build a Stock Reinforcement Learning Algorithm
Build a intelligent car that can complete various environments
And much more!
This course is for you if ...
You're interested in cutting edge technology and applying it in practical ways
You're passionate about Deep Learning/AI
Want to learn about cutting-edge technologies!
Want to learn reinforcement learning by doing cool projects!
Course prerequisites:
Python!
Who this course is for:
Python Developers
Coding Enthusiast
People Interested in Cutting-Edge Technology