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

Real-time strategy games

The term real-time strategy (RTS) was first used by Brett Sperry as a tagline to market their game Dune II. Real-time strategy games involve the player using real-time tactics to increase assets, and save them, and utilizing them to destroy the assets of the opponent. It is associated with the many complex tactical decisions that need to be taken in a very short period of time. 

This is different from turn-based strategy games, where each opponent has time to analyze and take action while other opponents couldn't perform any actions. In real-time strategy games, the action and reaction both take place in real time, since the other entities in the environment, that is, opponents, are also active and will be performing actions simultaneously. In a real strategy game environment, there are varied forms of entities, which include players, structures, and their varied high dimensional features. Thus, the goal would be to take the optimal actions to survive in the gaming...