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...
Hands-On Intelligent Agents with OpenAI Gym
By :
Hands-On Intelligent Agents with OpenAI Gym
By:
Overview of this book
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.
Related Content you might be interested in
Current Title:
Hands-On Intelligent Agents with OpenAI Gym
Table of Contents (12 chapters)
Preface
Free Chapter
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)
Other Books You May Enjoy
Customer Reviews