Lapan Deep Reinforcement Learning Hands-On

Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
2. Auflage 2020
ISBN: 978-1-83882-004-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 826 Seiten

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

Autoren/Hrsg.

Lapan, Maxim

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

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
- Higher-Level RL libraries
- DQN Extensions
- Ways to Speed up RL
- Stocks Trading Using RL
- Policy Gradients
- The Actor-Critic Method
- Asynchronous Advantage Actor-Critic
- Training Chatbots with RL
- The TextWorld environment
- Web Navigation
- Continuous Action Space
- RL in Robotics
- Trust Regions
- Black-Box Optimization in RL
- Advanced exploration
- Beyond Model-Free
- AlphaGo Zero
- RL in Discrete Optimisation
- Multi-agent RL

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.