Book Image

Hands-On Intelligent Agents with OpenAI Gym

By : Palanisamy P
Book Image

Hands-On Intelligent Agents with OpenAI Gym

By: Palanisamy P

Overview of this book

Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
Table of Contents (12 chapters)

Practical reinforcement learning

Now that you have an intuitive understanding of what AI really means and the various classes of algorithm that drive its development, we will now focus on the practical aspects of building a reinforcement learning machine.

Here are the core concepts that you need to be aware of to develop reinforcement learning systems:

  • Agent
  • Rewards
  • Environment
  • State
  • Value function
  • Policy


In the reinforcement learning world, a machine is run or instructed by a (software) agent. The agent is the part of the machine that possesses intelligence and makes decisions on what to do next. You will come across the term "agent" several times as we dive deeper into reinforcement learning. Reinforcement...