Combine the power of Reinforcement Learning, Deep Learning and Machine Learning to create powerful real-world apps
About This 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 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
Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.