This book will guide you through the process of implementing your own intelligent agents to solve both discrete- and continuous-valued sequential decision-making problems with all the essential building blocks to develop, debug, train, visualize, customize, and test your intelligent agent implementations in a variety of learning environments, ranging from the Mountain Car and Cart Pole problems to Atari games and CARLA – an advanced simulator for autonomous driving. If you are someone wanting to get a head start in the 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.
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