Book Image

Reinforcement Learning with TensorFlow

By : Sayon Dutta
Book Image

Reinforcement Learning with TensorFlow

By: Sayon Dutta

Overview of this book

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.
Table of Contents (21 chapters)
Title Page
Packt Upsell

Why reinforcement learning?

In 2014, Google acquired a London-based startup named DeepMind for a whopping $500 million. In the news, we read that they had created an AI agent to beat any Atari game, but the main reason why Google paid so much to acquire it was because this breakthrough was a step closer toward general artificial intelligence. General artificial intelligence is referred to as an AI agent. It is capable of doing a variety of tasks and generalizing just like a human. When it surpasses that, that point of singularity is termed, artificial super intelligence. At present, the work done by the AI community is what we term, artificial narrow intelligence, where an AI agent is capable of acing a couple of tasks but not able to generalize over a variety of tasks. 

DeepMind published their paper, Human Level Control through Deep Reinforcement Learning in the research journal Nature showing that their deep reinforcement learning...