Hands-On Intelligent Agents With OpenAI Gym (HOIAWOG)!: Your guide to developing AI agents using Deep Reinforcement Learning
Discrete prediction and control problems include higher-level or behavioral decision making where the space of available actions to take or decisions to make is discrete and countable. Several real-world problems can be simplified if not solved, using intelligent agents that can learn and adapt to make optimal discrete decisions or actions. The capability to deal with continuous-valued spaces enables more finer control. Intelligent software agents that can learn to make optimal continuous-valued decisions or actions enable a vast majority of problems and tasks to be approachable and solvable by machines.
If you are someone wanting to get a head start in this direction of building intelligent agents to solve problems and you are looking for a structured yet concise and hands-on approach to follow, you will enjoy this book and the code repository. The chapters in this book and the accompanying code repository is aimed at being simple to understand and easy to follow. While simple language is used everywhere possible to describe the algorithms, the core theoretical concepts including the mathematical equations are laid out with brief and intuitive explanations as they are essential for understanding the code implementation and for further modifications and tailoring by the readers.
Examples of agents you will learn to develop:

The book begins by introducing the readers to learning based intelligent agents, environments to train these agents and the tools and frameworks necessary to implement these agents. In particular, the book concentrates on deep reinforcement learning based intelligent agents that combine deep learning and reinforcement learning. The learning environments, which define the problem to be solved or the tasks to be completed, used in the book are based on the open source, OpenAI Gym library. PyTorch is the deep learning framework used for the learning agent implementations. All the code and scripts necessary to follow the book chapter-by-chapter are made available at the following GitHub repository: Hands-On-Intelligent-Agents-With-OpenAI-Gym.
You can find the book on:

Amazon | Google Books | Packt | Play Store | Safari |
The book takes the readers through the step-by-step process of building intelligent agent algorithms using deep reinforcement learning starting from implementation of the building blocks for configuring, training, logging, visualizing, testing and monitoring the agent. The book walks the reader through agent implementations in PyTorch to solve a variety of tasks and problems including: classical AI problems and console games like the Atari games, and complex problems like autonomous driving in the CARLA driving simulator. 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 the readers to take their hands-on skills to the next level. The code repository for the book on GitHub provides all the necessary code, scripts and instructions to follow through the book successfully.
Learning outcome
- Get introduced to intelligent agents and learning environments
- Understand the basics of Reinforcement Learning (RL) & deep RL
- Get started with OpenAI gym & PyTorch for deep RL
- Implement building blocks to configure, train, log, visualize & test agents
- Implement deep Q learning agent to solve discrete optimal control tasks
- Learn to create custom learning environments for real-world problems
- Implement deep actor-critic agent to drive a car autonomously in CARLA
- Resources to take your intelligent agent development skills to next level
A chapter-wise summary of what is covered in the book is available here:

