Book Image

Hands-On Intelligent Agents with OpenAI Gym

By : Palanisamy P
Book Image

Hands-On Intelligent Agents with OpenAI Gym

By: Palanisamy P

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.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to Intelligent Agents and Learning Environments
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
2
Reinforcement Learning and Deep Reinforcement Learning
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
3
Getting Started with OpenAI Gym and Deep Reinforcement Learning
Getting Started with OpenAI Gym and Deep Reinforcement Learning
Code repository, setup, and configuration
Installing tools and libraries needed for deep reinforcement learning
Summary
4
Exploring the Gym and its Features
Exploring the Gym and its Features
Exploring the list of environments and nomenclature
Understanding the Gym interface
Spaces in the Gym
Summary
5
Implementing your First Learning Agent - Solving the Mountain Car problem
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
6
Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
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
7
Creating Custom OpenAI Gym Environments - CARLA Driving Simulator
Creating Custom OpenAI Gym Environments - CARLA Driving Simulator
Understanding the anatomy of Gym environments
Creating an OpenAI Gym-compatible CARLA driving simulator environment
Summary
8
Implementing an Intelligent - Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
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
9
Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
Gym interface-compatible environments
Other open source Python-based learning environments
Summary
10
Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
Deep Deterministic Policy Gradients
Proximal Policy Optimization
Rainbow
Summary
11
Other Books You May Enjoy
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Monte Carlo learning and temporal difference learning

At this point, we understand that it is very useful for an agent to learn the state value function , which informs the agent about the long-term value of being in state so that the agent can decide if it is a good state to be in or not. The Monte Carlo (MC) and Temporal Difference (TD) learning methods enable an agent to learn that!

The goal of MC and TD learning is to learn the value functions from the agent's experience as the agent follows its policy .

The following table summarizes the value estimate's update equation for the MC and TD learning methods:

Learning method State-value function
Monte Carlo
Temporal Difference

MC learning updates the value towards the actual return , which is the total discounted reward from time step t. This means that until the end. It is important to note that we...

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