Ravichandiran Deep Reinforcement Learning with Python

Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow
2. Auflage 2020
ISBN: 978-1-83921-559-9
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 760 Seiten

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

Autoren/Hrsg.

Ravichandiran, Sudharsan

Fachgebiete

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

Inhaltsverzeichnis

Table of Contents - Fundamentals of Reinforcement Learning
- A Guide to the Gym Toolkit
- The Bellman Equation and Dynamic Programming
- Monte Carlo Methods
- Understanding Temporal Difference Learning
- Case Study – The MAB Problem
- Deep Learning Foundations
- A Primer on TensorFlow
- Deep Q Network and Its Variants
- Policy Gradient Method
- Actor-Critic Methods – A2C and A3C
- Learning DDPG, TD3, and SAC
- TRPO, PPO, and ACKTR Methods
- Distributional Reinforcement Learning
- Imitation Learning and Inverse RL
- Deep Reinforcement Learning with Stable Baselines
- Reinforcement Learning Frontiers
- Appendix 1 – Reinforcement Learning Algorithms
- Appendix 2 – Assessments

Über Autor(innen)

Ravichandiran Sudharsan:

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.