A Detailed Step-by-Step Guide covering Reinforcement Learning concepts, techniques and various frameworks to develop self learning systems
About This Book
Become familiar with reinforcement learning concepts and learn how to implement them using TensorFlow
Implement different problem-solving methods for Reinforcement Learning such as dynamic programming, Monte Carlo methods, and more
Explore various reinforcement earning use-cases such as autonomous driving cars, robobrokers, and learning robots
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
What You Will Learn
Explore the applications of reinforcement learning in advertisement, image processing, and NLP
Master various aspects of RL such as Deep-Q-Network, A3C, Q Learning, and more
How Reinforcement Learning can be applied to robotics, autonomous vehicles, and finance.
Frameworks and technologies to implement the various RL mechanisms
Implement state-of-the-art RL algorithms from the basics
Build pipelines, systems, and applications using RL techniques
Teach an RL network to play a game using TensorFlow and/or the OpenAI gym framework
Develop new systems that can learn, understand the environment, and make decisions
Reinforcement Learning (RL) is the next emerging area in the space of Artificial Intelligence and 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. Furthermore, it show readers how to put the concepts to practical use with the help of TensorFlow and OpenAI Gym to train efficient deep reinforcement learning neural networks. The book also discusses reinforcement learning and the rewarding system: Markov Decision Processes (MDPs), Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learnings such as Q-learning and SARSA-
We see how reinforcement learning algorithms play a role in image processing and NLP, and how they can be used with TensorFlow and OpenAI Gym to build simple neural network models.
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.