Lapan Deep Reinforcement Learning Hands-On

Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
1. Auflage 2018
ISBN: 978-1-78883-930-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 546 Seiten

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

Autoren/Hrsg.

Lapan, Maxim

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Inhaltsverzeichnis

Table of Contents - What is Reinforcement Learning?
- OpenAI Gym
- Deep Learning with PyTorch
- The Cross-Entropy Method
- Tabular Learning and the Bellman Equation
- Deep Q-Networks
- DQN Extensions
- Stocks Trading Using RL
- Policy Gradients – An Alternative
- The Actor-Critic Method
- Asynchronous Advantage Actor-Critic
- Chatbots Training with RL
- Web Navigation
- Continuous Action Space
- Trust Regions – TRPO, PPO, and ACKTR
- Black-Box Optimization in RL
- Beyond Model-Free – Imagination
- AlphaGo Zero

Über Autor(innen)

Lapan Maxim:

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.