Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow

Key Features

• Learn reinforcement learning concepts and their implementation using TensorFlow
• Discover different problem-solving methods for Reinforcement Learning
• Apply reinforcement learning for 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 is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions.

The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, 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 a firm understanding of what reinforcement learning is and 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 techniques of Reinforcement Learning such as MDP, Q Learning and more
• Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP
• Teach a Reinforcement Learning model to play a game using TensorFlow and the 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 with 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 it’s 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