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Home > Data > Artificial Intelligence > Hands-On Intelligent Agents with OpenAI Gym
Hands-On Intelligent Agents with OpenAI Gym
Hands-On Intelligent Agents with OpenAI Gym

Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning

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Hands-On Intelligent Agents with OpenAI Gym

Reinforcement Learning and Deep Reinforcement Learning

This chapter provides a concise explanation of the basic terminology and concepts in reinforcement learning. It will give you a good understanding of the basic reinforcement learning framework for developing artificial intelligent agents. This chapter will also introduce deep reinforcement learning and provide you with a flavor of the types of advanced problems the algorithms enable you to solve. You will find mathematical expressions and equations used in quite a few places in this chapter. Although there's enough theory behind reinforcement learning and deep reinforcement learning to fill a whole book, the key concepts that are useful for practical implementation are discussed in this chapter, so that when we actually implement the algorithms in Python to train our agents, you can clearly understand the logic behind...

What is reinforcement learning?

If you are new to the field of Artificial Intelligence (AI) or machine learning, you might be wondering what reinforcement learning is all about. In simple terms, it is learning through reinforcement. Reinforcement, as you know from general English or psychology, is the act of increasing or strengthening the choice to take a particular action in response to something, because of the perceived benefit of receiving higher rewards for taking that action. We humans are good at learning through reinforcement from a very young age. Those who have kids may be utilizing this fact more often to teach good habits to them. Nevertheless, we will all be able to relate to this, because not so long ago we all went through that phase of life! Say parents reward their kid with chocolate if the kid completes their homework on time after school every day. The kid...

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

The intelligence demonstrated by humans and animals is called natural intelligence, but the intelligence demonstrated by machines is called AI, for obvious reasons. We humans develop algorithms and technologies that provide intelligence to machines. Some of the greatest developments on this front are in the fields of machine learning, artificial neural networks, and deep learning. These fields collectively drive the development of AI. There are three main types of machine learning paradigms that have been developed to some reasonable level of maturity so far, and they are the following:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

In the following diagram, you can get an intuitive picture of the field of AI. You can see that these learning paradigms are subsets of the field of machine learning...

Practical reinforcement learning

Now that you have an intuitive understanding of what AI really means and the various classes of algorithm that drive its development, we will now focus on the practical aspects of building a reinforcement learning machine.

Here are the core concepts that you need to be aware of to develop reinforcement learning systems:

  • Agent
  • Rewards
  • Environment
  • State
  • Value function
  • Policy

Agent

In the reinforcement learning world, a machine is run or instructed by a (software) agent. The agent is the part of the machine that possesses intelligence and makes decisions on what to do next. You will come across the term "agent" several times as we dive deeper into reinforcement learning. Reinforcement...

Markov Decision Process

A Markov Decision Process (MDP) provides a formal framework for reinforcement learning. It is used to describe a fully observable environment where the outcomes are partly random and partly dependent on the actions taken by the agent or the decision maker. The following diagram is the progression of a Markov Process into a Markov Decision Process through the Markov Reward Process:

These stages can be described as follows:

  • A Markov Process (or a markov chain) is a sequence of random states s1, s2,... that obeys the Markov property. In simple terms, it is a random process without any memory about its history.
  • A Markov Reward Process (MRP) is a Markov Process (also called a Markov chain) with values.
  • A Markov Decision Process is a Markov Reward Process with decisions.

Planning with dynamic programming

Dynamic programming is a very general method to efficiently solve problems that can be decomposed into overlapping sub-problems. If you have used any type of recursive function in your code, you might have already got some preliminary flavor of dynamic programming. Dynamic programming, in simple terms, tries to cache or store the results of sub-problems so that they can be used later if required, instead of computing the results again.

Okay, so how is that relevant here, you may ask. Well, they are pretty useful for solving a fully defined MDP, which means that an agent can find the most optimal way to act in an environment to achieve the highest reward using dynamic programming if it has full knowledge of the MDP! In the following table, you will find a concise summary of what the inputs and outputs are when we are interested in sequential prediction...

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...

SARSA and Q-learning

It is also very useful for an agent to learn the action value function , which informs the agent about the long-term value of taking action in state so that the agent can take those actions that will maximize its expected, discounted future reward. The SARSA and Q-learning algorithms enable an agent to learn that! The following table summarizes the update equation for the SARSA algorithm and the Q-learning algorithm:

Learning method Action-value function

SARSA

Q-learning

SARSA is so named because of the sequence State->Action->Reward->State'->Action' that the algorithm's update step depends on. The description of the sequence goes like this: the agent, in state S, takes an action A and gets a reward R, and ends up in the next state S', after which the agent decides to take an action A' in the new state...

Deep reinforcement learning

With a basic understanding of reinforcement learning, you are now in a better state (hopefully you are not in a strictly Markov state where you have forgotten the history/things you have learned so far) to understand the basics of the cool new suite of algorithms that have been rocking the field of AI in recent times.

Deep reinforcement learning emerged naturally when people made advancements in the deep learning field and applied them to reinforcement learning. We learned about the state-value function, action-value function, and policy. Let's briefly look at how they can be represented mathematically or realized through computer code. The state-value function is a real-value function that takes the current state as the input and outputs a real-value number (such as 4.57). This number is the agent's prediction of how good it is to be in...

Practical applications of reinforcement and deep reinforcement learning algorithms

Until recently, practical applications of reinforcement learning and deep reinforcement learning were limited, due to sample complexity and instability. But, these algorithms proved to be quite powerful in solving some really hard practical problems. Some of them are listed here to give you an idea:

  • Learning to play video games better than humans: This news has probably reached you by now. Researchers at DeepMind and others developed a series of algorithms, starting with DeepMind's Deep-Q-Network, or DQN for short, which reached human-level performance in playing Atari games. We will actually be implementing this algorithm in a later chapter of this book! In essence, it is a deep variant of the Q-learning algorithm we briefly saw in this chapter, with a few changes that increased the speed...

Summary

In this chapter, we discussed how an agent interacts with an environment by taking an action based on the observation it receives from the environment, and the environment responds to the agent's action with an (optional) reward and the next observation.

With a concise understanding of the foundations of reinforcement learning, we went deeper to understand what deep reinforcement learning is, and uncovered the fact that we could use deep neural networks to represent value functions and policies. Although this chapter was a little heavy on notation and definitions, hopefully it laid a strong foundation for us to develop some cool agents in the upcoming chapters. In the next chapter, we will consolidate our learning in the first two chapters and put it to use by laying out the groundwork to train an agent to solve some interesting problems.

...
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Key benefits

  • 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

Description

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.

Who is this book for?

If you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.

What you will learn

  • 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
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Publication date : Jul 31, 2018
Length: 254 pages
Edition : 1st
Language : English
ISBN-13 : 9781788836579
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Category :
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Language : English
ISBN-13 : 9781788836579
Vendor :
OpenAI
Category :
Data
Languages :
Python
Concepts :
Artificial Intelligence
Tools :
Deep Reinforcement Learning

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Table of Contents

11 Chapters
Introduction to Intelligent Agents and Learning Environments Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
Deep Deterministic Policy Gradients
Proximal Policy Optimization
Rainbow
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Customer reviews

Rating distribution
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
(3 Ratings)
5 star 0%
4 star 33.3%
3 star 0%
2 star 0%
1 star 66.7%
baheri Oct 26, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
At some extend helped me to solve problem.
Amazon Verified review Amazon
Wilhem Kornhauser Jul 09, 2021
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Trying to install this onto a Ubuntu machine is anything but simple. The bash line commands given in the book do not install all of the required files for setup. I had to seek out an addition .yaml file from Github. Then, the numpy version installed using the given commands would not match the "updated" .yaml file specifications from the authors Github. I got that resolved, but am facing issues with the roboschool version. Given how many issues I am seeing with the setup, I have major doubts that the code in this book will execute smoothly and allow me to actually have a hands-on experience with intelligent agents in OpenAI Gym; I'm really having a Hands-On time with Stack Overflow.
Amazon Verified review Amazon
Matthew R. Versaggi Mar 27, 2022
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I'm in the AI space dealing with Intelligent Agents for my daily job - the title of this book thus spoke to me, but the implementation of it left me seriously doubting the value of fighting through the setup of the experiments. The environment setup on Ubuntu 20 is non-trivial, and the library version issues have consumed hours of my time with a persistent issue of "No module named 'gym-envs.stari' " that hasn't even adequately been solved with authority online just yet. It just seemed like the book was slapped together w/out the engineering thought on how others would be able to engage in the technical experiments and actually get something valuable from it. It's a pity - I had such high hopes for this book.
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About the author

Profile icon Palanisamy
Palanisamy
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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.
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  17. Venezuela
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Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela