Hands-On Intelligent Agents with OpenAI Gym

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

Hands-On Intelligent Agents with OpenAI Gym

Praveen Palanisamy

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
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Book Details

ISBN 139781788836579
Paperback254 pages

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.

Table of Contents

Chapter 9: Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
Chapter 10: Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)

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

Authors

Table of Contents

Chapter 9: Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
Chapter 10: Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)

Book Details

ISBN 139781788836579
Paperback254 pages
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