Hands-On Intelligent Agents with OpenAI Gym

By : Palanisamy

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Buy this Book

Hands-On Intelligent Agents with OpenAI Gym

2 (3)

By: Palanisamy

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction to Intelligent Agents and Learning Environments

What is an intelligent agent?

Learning environments

What is OpenAI Gym?

Understanding the features of OpenAI Gym

What can you do with the OpenAI Gym toolkit?

Creating your first OpenAI Gym environment

Summary

Reinforcement Learning and Deep Reinforcement Learning

What is reinforcement learning?

Understanding what AI means and what's in it in an intuitive way

Practical reinforcement learning

Markov Decision Process

Planning with dynamic programming

Monte Carlo learning and temporal difference learning

SARSA and Q-learning

Deep reinforcement learning

Practical applications of reinforcement and deep reinforcement learning algorithms

Summary

Getting Started with OpenAI Gym and Deep Reinforcement Learning

Code repository, setup, and configuration

Installing tools and libraries needed for deep reinforcement learning

Summary

Exploring the Gym and its Features

Exploring the list of environments and nomenclature

Understanding the Gym interface

Spaces in the Gym

Summary

Implementing your First Learning Agent - Solving the Mountain Car problem

Understanding the Mountain Car problem

Implementing a Q-learning agent from scratch

Training the reinforcement learning agent at the Gym

Testing and recording the performance of the agent

A simple and complete Q-Learner implementation for solving the Mountain Car problem

Summary

Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning

Improving the Q-learning agent

Implementing a deep Q-learning agent

The Atari Gym environment

Training the deep Q-learner to play Atari games

Summary

Creating Custom OpenAI Gym Environments - CARLA Driving Simulator

Understanding the anatomy of Gym environments

Creating an OpenAI Gym-compatible CARLA driving simulator environment

Summary

Implementing an Intelligent - Autonomous Car Driving Agent using Deep Actor-Critic Algorithm

The deep n-step advantage actor-critic algorithm

Implementing a deep n-step advantage actor critic agent

Training an intelligent and autonomous driving agent

Summary

Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab

Gym interface-compatible environments

Other open source Python-based learning environments

Summary

Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)

Deep Deterministic Policy Gradients

Proximal Policy Optimization

Rainbow

Summary

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Rainbow

Rainbow (https://arxiv.org/pdf/1710.02298.pdf) is an off-policy deep reinforcement learning algorithm based on DQN. We looked at and implemented deep Q-learning (DQN) and some of the extensions to DQN in Chapter 6, Implementing an Intelligent Agent for Optimal Discrete Control Using Deep Q-Learning. There have been several more extensions and improvements to the DQN algorithm. Rainbow combines six of those extensions and shows that the combination works much better. Rainbow is a state-of-the art algorithm that currently holds the record for the highest score on all Atari games. If you are wondering why the algorithm is named Rainbow, it is most probably due to the fact that it combines seven (the number of colors in a rainbow) extensions to the Q-learning algorithm, namely: