Reinforcement Learning beginner to master - AI in Python
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: A2C, REINFORCE, DQN, etc.
Product Brand: Udemy
4.5
Reinforcement Learning beginner to master - AI in Python
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: A2C, REINFORCE, DQN, etc.
Product Brand: Udemy
4.5
Udemy Coupon Code for Reinforcement Learning beginner to master – AI in Python Course. Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: A2C, REINFORCE, DQN, etc.
Created by Escape Velocity Labs | 10.5 hours on-demand video course
Reinforcement Learning Course Overview
Reinforcement Learning beginner to master – AI in Python Courses Coupon Code. This is the most complete Reinforcement Learning course on Udemy. In it you will learn the basics of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will also learn to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning.
This course will give you the foundation you need to be able to understand new algorithms as they emerge. It will also prepare you for the next courses in this series, in which we will go much deeper into different branches of Reinforcement Learning and look at some of the more advanced algorithms that exist.
What you’ll learn
- Understand the Reinforcement Learning paradigm and the tasks that it’s best suited to solve.
- Understand the process of solving a cognitive task using Reinforcement Learning
- Understand the different approaches to solving a task using Reinforcement Learning and choose the most fitting
- Implement Reinforcement Learning algorithms completely from scratch
- Fundamentally understand the learning process for each algorithm
- Debug and extend the algorithms presented
- Understand and implement new algorithms from research papers