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)


Rainbow ( is an off-policy deep reinforcement learning algorithm based on DQN. We looked at and implemented deep Q-learning (DQN) and some of the extensions to DQN in Chapter 6, Implementing an Intelligent Agent for Optimal Discrete Control Using Deep Q-Learning. There have been several more extensions and improvements to the DQN algorithm. Rainbow combines six of those extensions and shows that the combination works much better. Rainbow is a state-of-the art algorithm that currently holds the record for the highest score on all Atari games. If you are wondering why the algorithm is named Rainbow, it is most probably due to the fact that it combines seven (the number of colors in a rainbow) extensions to the Q-learning algorithm, namely:

  • DQN
  • Double Q-Learning
  • Prioritized experience replay
  • Dueling networks
  • Multi-step learning/n-step...