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

Reinforcement learning and other approaches

There have been many approaches devised for solving the problem of real-time strategy gaming. One of the major approaches before reinforcement learning was online case-based planning. Online case-based planning involves real-time case-based reasoning. In a case-based reasoning, a set of methods are used to learn the plans. Online case-based planning implemented this property along with the implementation of plan acquisition and execution, and that too in real time.

Online case-based planning

Case-based reasoning consists of four steps:

  • Retrieve

  • Reuse

  • Revise

  • Retain

These steps are illustrated in the following image:

Case-based reasoning

In the retrieval step, a subset of cases that are relevant to the problem are selected from the case base. In the reuse step, the solution as per the cases selected is adapted. Then, in the revision step, the adapted solution is verified through testing it in a real-world environment and observes a feedback quantifying the...