Lanham Hands-On Reinforcement Learning for Games

Implementing self-learning agents in games using artificial intelligence techniques
1. Auflage 2020
ISBN: 978-1-83921-677-0
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 432 Seiten

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

Autoren/Hrsg.

Lanham, Micheal

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

Table of Contents - Understanding Rewards-Based Learning
- Dynamic Programming and the Bellman Equation
- Monte Carlo Methods
- Temporal Difference Learning
- Exploring SARSA
- Going Deep with DQN
- Going Deeper with DDQN
- Policy Gradient Methods
- Optimizing for Continuous Control
- All about Rainbow DQN
- Exploiting ML-Agents
- DRL Frameworks
- 3D Worlds
- From DRL to AGI

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.