Hands-On Deep Q-Learning [Video]

More Information
Learn
  • Get grips on various Reinforcement Learning techniques while building Artificial Intelligence using PYTORCH, Kivy and OpenAIGym
  • A solid understanding of Deep Q-Learning intuitions and its functioning
  • Optimize performance and efficiency by implementing Deep Q-Learning
  • Create a virtual Self Driving Car application with Deep Q-Learning
  • Make an Intelligence to win the game named DOOM using Deep Convolutional Q-Learning
  • Understand the working behind Artificial Intelligence
About

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 may be wondering that why we create artificial intelligence in a game environment. That is because, once we have created our artificial intelligence in a gaming environment with the help of OpenAIGym, we can use those same principles to solve complex real-world problems just by changing and tweaking algorithm parameters. Get your hands on this course to learn the most fascinating technology in the field of Artificial Intelligence and leverage the power of Reinforcement Learning right away!

You can find the code for this course on GitHub: https://github.com/PacktPublishing/-Hands-On-Deep-Q-Learning/settings/collaboration

Style 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!

Features
  • Combine the power of Reinforcement Learning, Deep Learning, and Machine Learning to create powerful AI for real-world applications
  • Master Facebook's PYTORCH framework, Kivy, and OpenAI
  • Get hands-on experience of Facebook's PYTORCH framework, Kivy and OpenAIGym(Founded by Elon Musk) by creating Artificial Intelligence using Deep Q-Learning and Deep Convolutional Q-Learning
  • This course is designed with minimal theory and maximal practical implementation (followed by step-by-step instructions) to get you up-and-running.
Course Length 1 hour 53 minutes
ISBN 9781789957549
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

Authors

Kaiser Hamid Rabbi

Kaiser Hamid Rabbi is a Data Scientist who is super-passionate about Artificial Intelligence, Machine Learning, and Data Science. He has entirely devoted himself to learning more about Big Data Science technologies such as Python, Machine Learning, Deep Learning, Artificial Intelligence, Reinforcement Learning, Data Mining, Data Analysis, Recommender Systems and so on over the last 4 years. Kaiser also has a huge interest in Lygometry (things we know we do not know!) and always tries to understand domain knowledge based on his project experience as much as possible.
LinkedIn - https://www.linkedin.com/in/kaiserhamidrabbi/