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
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Chapter 7. Robo Everything – Real Strategy Gaming

In recent times, the video gaming industry has grown at a tremendous rate. As per the 2017 year in review report by SuperData, the global gaming industry generated revenue of $108.4 billion. Newzoo, a global gaming market researcher forecast that the revenue of the video gaming industry will exceed $140 billion by 2020. 

Real-time strategy games form a sub-category of the strategy video game genre and is now gaining higher importance relative to turn-based strategy games. In this chapter, we will discuss why the AI community is behind solving real-time strategy games and how reinforcement learning is better at solve this problem statement compared to the other algorithms in terms of learning and performance.

We will cover the following topics in this chapter:

  • Real-time strategy games
  • Reinforcement learning and other approaches
  • Reinforcement learning in RTS gaming