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 simulatorKey FeaturesExplore the OpenAI Gym toolkit and interface to use over 700 learning tasksImplement agents to solve simple to complex AI problemsStudy learning environments and discover how to create your ownBook DescriptionMany 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 learnExplore intelligent agents and learning environmentsUnderstand the basics of RL and deep RLGet started with OpenAI Gym and PyTorch for deep reinforcement learningDiscover deep Q learning agents to solve discrete optimal control tasksCreate custom learning environments for real-world problemsApply a deep actor-critic agent to drive a car autonomously in CARLAUse the latest learning environments and algorithms to upgrade your intelligent agent development skillsWho this book is forIf 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 EnvironmentsReinforcement Learning and Deep Reinforcement LearningGetting Started with OpenAI Gym and Deep Reinforcement LearningExploring the Gym and its FeaturesImplementing your First Learning Agent - Solving the Mountain Car problemImplementing an Intelligent Agent for Optimal Control using Deep Q-LearningCreating Custom OpenAI Gym Environments - Carla Driving SimulatorImplementing an Intelligent and u0026 Autonomous Car Driving Agent using Deep Actor-Critic AlgorithmExploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLabExploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)