Praveen Palanisamy - Hands-On Intelligent Agents with OpenAI Gym: Your Guide to Developing AI Agents Using Deep Reinforcement Learning

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Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.

Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He also authored bestseller Hands-On Reinforcement Learning with Python, published by Packt Publishing.

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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're a student, a game/machine learning developer, or an AI enthusiast looking to get started building intelligent agents and algorithms to solve a variety of problems using learning environments with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based, artificially intelligent agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python will help you get the most out of it.

Chapter 1, Introduction to Intelligent Agents and Learning Environments , which enables the development of several AI systems. It sheds light on the important features of the toolkit, which provides you with endless opportunities to create autonomous intelligent agents to solve several algorithmic tasks, games, and control tasks. By the end of this chapter, you will know enough to create an instance of a Gym environment using Python yourself.

Chapter 2, Reinforcement Learning and Deep Reinforcement Learning , provides a concise
explanation of the basic terminologies and concepts in reinforcement learning. The chapter
will give you a good understanding of the basic reinforcement learning framework for
developing AI agents. The chapter will also introduce deep reinforcement learning and
provide you with a flavor of the types of advanced problem the algorithms enable you to
solve.

Chapter 3, Getting Started with OpenAI Gym and Deep Reinforcement Learning , jumps right in and gets your development machine/computer ready with all the required installations and configurations needed for using the learning environments as well as PyTorch for developing deep learning algorithms.