Hands-On Deep Q-Learning [Video]
Do you want to build a virtual self-driving car AI application using the most cutting-edge algorithm of Reinforcement Learning: Deep Q-Learning? Do you want to create an intelligence that can win the famous 90's game—DOOM—by using Deep Convolutional Q-Learning? Deep Q-Learning is the most robust and powerful technique in Artificial Intelligence for solving complex real-world problems. Artificial Intelligence is making our lives easy day by day and reducing human effort everywhere in social media, websites, online stores, and even business. With a less talk and more action approach, this course will lead you through various implementations of Reinforcement Learning techniques by building a virtual self-driving car application and an AI to beat the monsters in DOOM.
You can find the code for this course on GitHub: https://github.com/PacktPublishing/-Hands-On-Deep-Q-Learning/settings/collaborationStyle and Approach
This hands-on course covers all the important aspects of Q-Learning, Deep Q-Learning and Deep Convolutional Q-Learning, the various fields of Reinforcement Learning. And we cover all of those topics by coding in PYTORCH, Kivy, and OpenAIGym. Throughout the course, we will build an intelligent self-driving car by applying Deep Q-Learning and we are going to win Doom with the power of Deep Convolutional Q-Learning!
|Course Length||1 hour 53 minutes|
|Date Of Publication||28 Feb 2019|
|Self Driving Car – Part 1|
|Self Driving Car – Part 2|
|Self Driving Car – Part 3|
|Playing with our SDC AI|
|Build an AI for DOOM – Part 1|
|Build an AI for DOOM – Part 2|
|Build an AI for DOOM – Part 3|
|Playing with our AI in DOOM|