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Home > Data > Deep Reinforcement Learning > Deep Reinforcement Learning Hands-On
Deep Reinforcement Learning Hands-On
Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF , Third Edition

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Deep Reinforcement Learning Hands-On

What Is Reinforcement Learning?

The automatic learning of optimal decisions over time is a general and common problem that has been studied in many scientific and engineering fields. In our changing world, even problems that look like static input-output problems can become dynamic if time is taken into account. For example, imagine that you want to solve the simple supervised learning problem of pet image classification with two target classes—dog and cat. You gather the training dataset and implement the classifier using your favorite deep learning toolkit. After the training and validation, the model demonstrates excellent performance. Great! You deploy it and leave it running for a while. However, after a vacation at some seaside resort, you return to discover that dog grooming fashions have changed and a significant portion of your queries are now misclassified, so you need to update your training images and repeat the process again. Not so great!

This...

Supervised learning

You may be familiar with the notion of supervised learning, which is the most studied and well-known ML problem. Its basic question is, how do you automatically build a function that maps some input into some output when given a set of example pairs? It sounds simple in those terms, but the problem includes many tricky questions that computers have only recently started to address with some success. There are lots of examples of supervised learning problems, including the following:

  • Text classification: Is this email message spam or not?

  • Image classification and object location: Does this image contain a picture of a cat, dog, or something else?

  • Regression problems: Given the information from weather sensors, what will be the weather tomorrow?

  • Sentiment analysis: What is the customer satisfaction level of this review?

These questions...

Unsupervised learning

At the other extreme, we have the so-called unsupervised learning, which assumes no supervision and has no known labels assigned to our data. The main objective is to learn some hidden structure of the dataset at hand. One common example of such an approach to learning is the clustering of data. This happens when our algorithm tries to combine data items into a set of clusters, which can reveal relationships in data. For instance, you might want to find similar images or clients with common behavior patterns.

Another unsupervised learning method that is becoming more and more popular is generative adversarial networks (GANs). When we have two competing neural networks (NNs), the first network tries to generate fake data to fool the second network, while the second network tries to discriminate artificially generated data from data sampled from our dataset. Over time, both networks become more and more skillful in their tasks by capturing subtle specific...

Reinforcement learning

RL is the third camp and lies somewhere in between full supervision and a complete lack of predefined labels. On the one hand, it uses many well-established methods of supervised learning, such as deep neural networks for function approximation, stochastic gradient descent, and backpropagation, to learn data representation. On the other hand, it usually applies them in a different way.

In the next two sections of the chapter, we will explore specific details of the RL approach, including assumptions and abstractions in its strict mathematical form. For now, to compare RL with supervised and unsupervised learning, we will take a less formal, but more easily understood, path.

Imagine that you have an agent that needs to take actions in some environment. Both “agent” and “environment” will be defined in detail later in this chapter. A robot mouse in a maze is a good example, but you can also imagine an automatic helicopter...

Complications in RL

The first thing to note is that observations in RL depend on an agent’s behavior and, to some extent, it is the result of this behavior. If your agent decides to do inefficient things, then the observations will tell you nothing about what it has done wrong and what should be done to improve the outcome (the agent will just get negative feedback all the time). If the agent is stubborn and keeps making mistakes, then the observations will give the false impression that there is no way to get a larger reward — life is suffering — which could be totally wrong.

In ML terms, this can be rephrased as having non-IID data. The abbreviation iid stands for independent and identically distributed, a requirement for most supervised learning methods.

The second thing that complicates our agent’s life is that it needs to not only exploit the knowledge it has learned, but actively explore the environment, because maybe doing things differently...

RL formalisms

Every scientific and engineering field has its own assumptions and limitations. Earlier in this chapter, we discussed supervised learning, in which such assumptions are the knowledge of input-output pairs. You have no labels for your data? You need to figure out how to obtain labels or try to use some other theory. This doesn’t make supervised learning good or bad; it just makes it inapplicable to your problem.

There are many historical examples of practical and theoretical breakthroughs that have occurred when somebody tried to challenge rules in a creative way. However, we also must understand our limitations. It’s important to know and understand game rules for various methods, as it can save you tons of time in advance. Of course, such formalisms exist for RL, and we will spend the rest of this book analyzing them from various angles.

The following diagram shows two major RL entities — agent and environment — and their communication...

The theoretical foundations of RL

In this section, I will introduce you to the mathematical representation and notation of the formalisms (reward, agent, actions, observations, and environment) that we just discussed. Then, using this as a knowledge base, we will explore the second-order notions of the RL language, including state, episode, history, value, and gain, which will be used repeatedly to describe different methods later in the book.

Markov decision processes

Before that, we will cover Markov decision processes (MDPs), which will bedescribed like a Russian matryoshka doll: we will start from the simplest case of a Markov process (MP), then extend that with rewards, which will turn it into a Markov reward process (MRP). Then, we will put this idea into an extra envelope by adding actions, which will lead us to an MDP.

MPs and MDPs are widely used in computer science and other engineering fields. So, reading this chapter will be useful for...

Summary

In this chapter, you started your journey into the RL world by learning what makes RL special and how it relates to the supervised and unsupervised learning paradigms. We then learned about the basic RL formalisms and how they interact with each other, after which we covered MPs, MRPs, and MDPs. This knowledge will be the foundation for the material that we will cover in the rest of the book.

In the next chapter, we will move away from the formal theory to the practice of RL. We will cover the setup required and libraries, and then you will write your first agent.

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Key benefits

  • Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation
  • Develop deep RL models, improve their stability, and efficiently solve complex environments
  • New content on RL from human feedback (RLHF), MuZero, and transformers

Description

Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the fi eld, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers. The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion

Who is this book for?

This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it’s also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and finance

What you will learn

  • Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs
  • Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG
  • Implement RL algorithms using PyTorch and modern RL libraries
  • Build and train deep Q-networks to solve complex tasks in Atari environments
  • Speed up RL models using algorithmic and engineering approaches
  • Leverage advanced techniques like proximal policy optimization (PPO) for more stable training
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Publication date : Nov 12, 2024
Length: 716 pages
Edition : 3rd
Language : English
ISBN-13 : 9781835882702
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Concepts :
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Language : English
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Python
Concepts :
Deep Reinforcement Learning
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Table of Contents

28 Chapters
Part 1 Introduction to RL Chevron down icon Chevron up icon
Part 1 Introduction to RL
What Is Reinforcement Learning? Chevron down icon Chevron up icon
What Is Reinforcement Learning?
Supervised learning
Unsupervised learning
Reinforcement learning
Complications in RL
RL formalisms
The theoretical foundations of RL
Summary
OpenAI Gym API and Gymnasium Chevron down icon Chevron up icon
OpenAI Gym API and Gymnasium
The anatomy of the agent
Hardware and software requirements
The OpenAI Gym API and Gymnasium
The random CartPole agent
Extra Gym API functionality
Summary
Deep Learning with PyTorch Chevron down icon Chevron up icon
Deep Learning with PyTorch
Tensors
The creation of tensors
Gradients
NN building blocks
Custom layers
Loss functions and optimizers
Monitoring with TensorBoard
GAN on Atari images
PyTorch Ignite
Summary
The Cross-Entropy Method Chevron down icon Chevron up icon
The Cross-Entropy Method
The taxonomy of RL methods
The cross-entropy method in practice
The cross-entropy method on CartPole
The cross-entropy method on FrozenLake
The theoretical background of the cross-entropy method
Summary
Part 2 Value-based methods Chevron down icon Chevron up icon
Part 2 Value-based methods
Tabular Learning and the Bellman Equation Chevron down icon Chevron up icon
Tabular Learning and the Bellman Equation
Value, state, and optimality
The Bellman equation of optimality
The value of the action
The value iteration method
Value iteration in practice
Q-iteration for FrozenLake
Summary
Deep Q-Networks Chevron down icon Chevron up icon
Deep Q-Networks
Real-life value iteration
Tabular Q-learning
Deep Q-learning
DQN on Pong
Things to try
Summary
Higher-Level RL Libraries Chevron down icon Chevron up icon
Higher-Level RL Libraries
Why RL libraries?
The PTAN library
The PTAN CartPole solver
Other RL libraries
Summary
DQN Extensions Chevron down icon Chevron up icon
DQN Extensions
Basic DQN
N-step DQN
Double DQN
Noisy networks
Prioritized replay buffer
Dueling DQN
Categorical DQN
Combining everything
Summary
Ways to Speed Up RL Chevron down icon Chevron up icon
Ways to Speed Up RL
Why speed matters
Baseline
The computation graph in PyTorch
Several environments
Playing and training in separate processes
Tweaking wrappers
Benchmark results
Summary
Stocks Trading Using RL Chevron down icon Chevron up icon
Stocks Trading Using RL
Why trading?
Problem statement and key decisions
Data
The trading environment
Models
Training code
Results
Things to try
Summary
Part 3 Policy-based methods Chevron down icon Chevron up icon
Part 3 Policy-based methods
Policy Gradients Chevron down icon Chevron up icon
Policy Gradients
Values and policy
The REINFORCE method
REINFORCE issues
Policy gradient methods on CartPole
Policy gradient methods on Pong
Summary
Actor-Critic Method: A2C and A3C Chevron down icon Chevron up icon
Actor-Critic Method: A2C and A3C
Variance reduction
CartPole variance
Advantage actor-critic (A2C)
Asynchronous Advantage Actor-Critic (A3C)
Summary
The TextWorld Environment Chevron down icon Chevron up icon
The TextWorld Environment
Interactive fiction
The environment
The deep NLP basics
Baseline DQN
Tweaking observations
Transformers
ChatGPT
Summary
Web Navigation Chevron down icon Chevron up icon
Web Navigation
The evolution of web navigation
Browser automation and RL
Challenges in browser automation
The MiniWoB benchmark
MiniWoB++
The simple clicking approach
Adding text description
Human demonstrations
Things to try
Summary
Part 4 Advanced RL Chevron down icon Chevron up icon
Part 4 Advanced RL
Continous Action Space Chevron down icon Chevron up icon
Continous Action Space
Why a continuous space?
The A2C method
Deep deterministic policy gradients
Distributional policy gradients
Things to try
Summary
Trust Region Methods Chevron down icon Chevron up icon
Trust Region Methods
Environments
The A2C baseline
PPO
TRPO
ACKTR
SAC
Overall results
Summary
Black-Box Optimizations in RL Chevron down icon Chevron up icon
Black-Box Optimizations in RL
Black-box methods
Evolution strategies
Genetic algorithms
Summary
Advanced Exploration Chevron down icon Chevron up icon
Advanced Exploration
Why exploration is important
What’s wrong with 𝜖-greedy?
Alternative ways of exploration
MountainCar experiments
Atari experiments
Summary
Reinforcement Learning with Human Feedback Chevron down icon Chevron up icon
Reinforcement Learning with Human Feedback
Reward functions in complex environments
Theoretical background
RLHF experiments
Summary
AlphaGo Zero and MuZero Chevron down icon Chevron up icon
AlphaGo Zero and MuZero
Comparing model-based and model-free methods
Model-based methods for board games
The AlphaGo Zero method
Connect 4 with AlphaGo Zero
MuZero
Connect 4 with MuZero
MuZero results
MuZero and Atari
Summary
RL in Discrete Optimization Chevron down icon Chevron up icon
RL in Discrete Optimization
The Rubik’s cube and discrete optimization
Optimality and God’s number
Approaches to cube solving
The training process
The model application
Results
The code outline
The experiment results
Further improvements and experiments
Summary
Multi-Agent RL Chevron down icon Chevron up icon
Multi-Agent RL
What is multi-agent RL?
Getting started with the environment
Deep Q-network for tigers
Collaboration by the tigers
Training both tigers and deer
The battle environment
Summary
Bibliography Chevron down icon Chevron up icon
Bibliography
Index Chevron down icon Chevron up icon
Index

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About the author

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Maxim Lapan
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Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
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On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on [email protected] 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.

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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