Bilgin Mastering Reinforcement Learning with Python

Build next-generation, self-learning models using reinforcement learning techniques and best practices
1. Auflage 2020
ISBN: 978-1-83864-849-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 532 Seiten

No detailed description available for "Mastering Reinforcement Learning with Python".

Autoren/Hrsg.

Bilgin, Enes

Fachgebiete

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

Inhaltsverzeichnis

Table of Contents - Introduction to Reinforcement Learning
- Multi-armed Bandits
- Contextual Bandits
- Makings of the Markov Decision Process
- Solving the Reinforcement Learning Problem
- Deep Q-Learning at Scale
- Policy Based Methods
- Model-Based Methods
- Multi-Agent Reinforcement Learning
- Machine Teaching
- Generalization and Domain Randomization
- Meta-reinforcement learning
- Other Advanced Topics
- Autonomous Systems
- Supply Chain Management
- Marketing, Personalization and Finance
- Smart City and Cybersecurity
- Challenges and Future Directions in Reinforcement Learning

Über Autor(innen)

Bilgin Enes:

Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.