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)

Training the deep Q-learner to play Atari games

We have gone through several new techniques in this chapter. You deserve a pat on your back for making it this far! Now starts the fun part where you can let your agents train by themselves to play several Atari games and see how they are progressing. What is great about our deep Q-learner is the fact that we can use the same agent to train and play any of the Atari games!

By the end of this section, you should be able to use our deep Q learning agent to observe the pixels on the screen and take actions by sending the joystick commands to the Atari Gym environment, just like what is shown in the following screenshot:

Putting together a comprehensive deep Q-learner

It is time...