Leverage the power of reinforcement learning techniques to develop self-learning systems using TensorFlow

Key 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 Description

Reinforcement 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 For

If 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.

Table of Contents

1. Deep Learning - Architectures and Frameworks
2. Training Reinforcement Learning Agents Using OpenAI Gym
3. Markov Decision Process (MDP)
4. Policy Gradients
5. Q-Learning & Deep Q Networks
6. Asynchronous Methods
7. Robo Everything - Real Strategy Gaming
8. AlphaGo - Reinforcement Learning at its Best
9. Reinforcement Learning in Autonomous Driving
10. Financial Portfolio Management
11. Reinforcement Learning in Robotics
12. Deep Reinforcement Learning in AdTech
13. Reinforcement Learning in Image Processing
14. Deep Reinforcement Learning in NLP
15. Appendix 1.Further Topics in Reinforcement Learning