Lanham Hands-On Deep Learning for Games

Leverage the power of neural networks and reinforcement learning to build intelligent games
1. Auflage 2019
ISBN: 978-1-78899-876-5
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 392 Seiten

No detailed description available for "Hands-On Deep Learning for Games".

Autoren/Hrsg.

Lanham, Micheal

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Table of Contents - Deep Learning for Games
- Convolutional and Recurrent Networks
- GAN for Games
- Building a Deep Learning Gaming Chatbot
- Introducing DRL
- Unity ML-Agents
- Agent and the Environment
- Understanding PPO
- Rewards and Reinforcement Learning
- Imitation and Transfer Learning
- Building Multi-Agent Environments
- Debugging/Testing a Game with DRL
- Obstacle Tower Challenge and Beyond

Über Autor(innen)

Lanham Micheal:

Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.