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


In this chapter, we discussed real strategy games and why researchers from the AI community are trying to solve them. We also covered the complexity and properties of real strategy games and the different traditional AI approaches, such as case-based reasoning and online case-based planning to solve them and their drawbacks. We discussed the reason behind reinforcement learning being the perfect candidate for the problem and how it is successful in fulfilling the complexity and issues related to real-time strategy games where earlier traditional AI approaches failed. We also learnt about deep autoencoders and how they can be used to reduce the dimensionality of the input data and obtain a better representation of the input.

In the next chapter, we will cover the most famous topic that brought deep reinforcement learning into the limelight and made it the flag bearer of AI algorithms, that is, Alpha Go.