Hands-On Intelligent Agents with OpenAI Gym

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  • 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

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.

  • 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
Page Count 254
Course Length 7 hours 37 minutes
ISBN 9781788836579
Date Of Publication 30 Jul 2018


Praveen Palanisamy

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.