Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator
Key Features
- Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks
- Implement agents to solve simple to complex AI problems
- Study learning environments and discover how to create your own
Book Description
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.
What you will learn
- Explore intelligent agents and learning environments
- Understand the basics of RL and deep RL
- Get started with OpenAI Gym and PyTorch for deep reinforcement learning
- Discover deep Q learning agents to solve discrete optimal control tasks
- Create custom learning environments for real-world problems
- Apply a deep actor-critic agent to drive a car autonomously in CARLA
- Use the latest learning environments and algorithms to upgrade your intelligent agent development skills
Who this book is for
If you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems 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 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 programming language will help you get the most out of it.
Table of Contents
- Introduction to Intelligent Agents and Learning Environments
- Reinforcement Learning and Deep Reinforcement Learning
- Getting Started with OpenAI Gym and Deep Reinforcement Learning
- Exploring the Gym and its Features
- Implementing your First Learning Agent – Solving the Mountain Car problem
- Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
- Creating Custom OpenAI Gym Environments – Carla Driving Simulator
- Implementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
- Exploring the Learning Environment Landscape – Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
- Exploring the Learning Algorithm Landscape – DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
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.
