Balakrishnan TensorFlow Reinforcement Learning Quick Start Guide

Get up and running with training and deploying intelligent, self-learning agents using Python
1. Auflage 2019
ISBN: 978-1-78953-344-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 184 Seiten

No detailed description available for "TensorFlow Reinforcement Learning Quick Start Guide".

Autoren/Hrsg.

Balakrishnan, Kaushik

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie

Inhaltsverzeichnis

Table of Contents - Up and Running with Reinforcement Learning

- Temporal Difference, SARSA, and Q-Learning
- Deep Q-Network

- Double DQN, Dueling Architectures, and Rainbow
- Deep Deterministic Policy Gradient
- Asynchronous Methods - A3C and A2C
- Trust Region Policy Optimization and Proximal Policy Optimization
- Deep RL Applied to Autonomous Driving

Über Autor(innen)

Balakrishnan Kaushik:

Kaushik Balakrishnan works for BMW in Silicon Valley, and applies reinforcement learning, machine learning, and computer vision to solve problems in autonomous driving. Previously, he also worked at Ford Motor Company and NASA Jet Propulsion Laboratory. His primary expertise is in machine learning, computer vision, and high-performance computing, and he has worked on several projects involving both research and industrial applications. He has also worked on numerical simulations of rocket landings on planetary surfaces, and for this he developed several high-fidelity models that run efficiently on supercomputers. He holds a PhD in aerospace engineering from the Georgia Institute of Technology in Atlanta, Georgia.