Dutta Reinforcement Learning with TensorFlow

A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym
1. Auflage 2024
ISBN: 978-1-78883-071-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 334 Seiten

Leverage the power of reinforcement learning techniques to develop self-learning systems using TensorFlowKey Features - * Explore reinforcement learning concepts and their implementation using TensorFlow
- * Discover different problem-solving methods for reinforcement learning
- * Apply reinforcement learning to autonomous driving cars, robobrokers, and more
Book DescriptionReinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions. The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym. What you will learn - * Implement state-of-the-art reinforcement learning algorithms from the basics
- * Discover various reinforcement learning techniques such as MDP, Q Learning, and more
- * Explore the applications of reinforcement learning in advertisement, image processing, and NLP
- * Teach a reinforcement learning model to play a game using TensorFlow and OpenAI Gym
- * Understand how reinforcement learning applications are used in robotics
Who this book is forIf you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience of reinforcement learning is required.

Autoren/Hrsg.

Dutta, Sayon

Fachgebiete

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

Inhaltsverzeichnis

Table of Contents - Deep Learning

- Training Reinforcement Learning Agents Using OpenAI Gym
- Markov Decision Process (MDP)
- Policy Gradients
- Q-Learning & Deep Q Networks

- Asynchronous Methods
- Robo Everything

- AlphaGo

- Reinforcement Learning in Autonomous Driving
- Financial Portfolio Management
- Reinforcement Learning in Robotics

- Deep Reinforcement Learning in AdTech
- Reinforcement Learning in Image Processing
- Deep Reinforcement Learning in NLP
- Appendix 1.Further topics in Reinforcement Learning

Über Autor(innen)

Dutta Sayon :

Sayon Dutta is an Artificial Intelligence researcher and developer. A graduate from IIT Kharagpur, he owns the software copyright for Mobile Irrigation Scheduler. At present, he is an AI engineer at Wissen Technology. He co-founded an AI startup Marax AI Inc., focused on AI-powered customer churn prediction. With over 2.5 years of experience in AI, he invests most of his time implementing AI research papers for industrial use cases, and weightlifting.