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Hands-On Intelligent Agents with OpenAI Gym

You're reading from   Hands-On Intelligent Agents with OpenAI Gym Your guide to developing AI agents using deep reinforcement learning

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788836579
Length 254 pages
Edition 1st Edition
Languages
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Author (1):
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Palanisamy Palanisamy
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Palanisamy
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Intelligent Agents and Learning Environments 2. Reinforcement Learning and Deep Reinforcement Learning FREE CHAPTER 3. Getting Started with OpenAI Gym and Deep Reinforcement Learning 4. Exploring the Gym and its Features 5. Implementing your First Learning Agent - Solving the Mountain Car problem 6. Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning 7. Creating Custom OpenAI Gym Environments - CARLA Driving Simulator 8. Implementing an Intelligent - Autonomous Car Driving Agent using Deep Actor-Critic Algorithm 9. Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab 10. Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based) 11. Other Books You May Enjoy

Training an intelligent and autonomous driving agent

We now have all the pieces we need to accomplish our goal for this chapter, which is to put together an intelligent, autonomous driving agent, and then train it to drive a car autonomously in the photo-realistic CARLA driving environment that we developed as a learning environment using the Gym interface in the previous chapter. The agent training process can take a while. Depending on the hardware of the machine that you are going to train the agent on, it may take anywhere from a few hours for simpler environments (such asPendulum-v0, CartPole-v0, and some of the Atari games) to a few days for complex environments (such as the CARLA driving environment). In order to first get a good understanding of the training process and how to monitor progress while the agent is training, we will start with a few simple examples to walk...

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