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Hands-On Reinforcement Learning with Python

By : Sudharsan Ravichandiran

2.6 (18)

Hands-On Reinforcement Learning with Python

2.6 (18)

By: Sudharsan Ravichandiran

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

What is RL?

RL algorithm

How RL differs from other ML paradigms

Elements of RL

Agent environment interface

Types of RL environment

RL platforms

Applications of RL

Summary

Questions

Further reading

Getting Started with OpenAI and TensorFlow

Getting Started with OpenAI and TensorFlow

Setting up your machine

OpenAI Gym

OpenAI Universe

TensorFlow

Summary

Questions

Further reading

The Markov Decision Process and Dynamic Programming

The Markov Decision Process and Dynamic Programming

The Markov chain and Markov process

Markov Decision Process

The Bellman equation and optimality

Solving the Bellman equation

Solving the frozen lake problem

Summary

Questions

Further reading

Gaming with Monte Carlo Methods

Gaming with Monte Carlo Methods

Monte Carlo methods

Monte Carlo prediction

Monte Carlo control

Summary

Questions

Further reading

Temporal Difference Learning

Temporal Difference Learning

TD learning

TD prediction

TD control

The difference between Q learning and SARSA

Summary

Questions

Further reading

Multi-Armed Bandit Problem

Multi-Armed Bandit Problem

The MAB problem

Applications of MAB

Identifying the right advertisement banner using MAB

Contextual bandits

Summary

Questions

Further reading

Deep Learning Fundamentals

Deep Learning Fundamentals

Artificial neurons

ANNs

Deep diving into ANN

Neural networks in TensorFlow

RNN

Long Short-Term Memory RNN

Convolutional neural networks

Classifying fashion products using CNN

Summary

Questions

Further reading

Atari Games with Deep Q Network

Atari Games with Deep Q Network

What is a Deep Q Network?

Architecture of DQN

Building an agent to play Atari games

Double DQN

Prioritized experience replay

Dueling network architecture

Summary

Questions

Further reading

Playing Doom with a Deep Recurrent Q Network

Playing Doom with a Deep Recurrent Q Network

DRQN

Training an agent to play Doom

DARQN

Summary

Questions

Further reading

The Asynchronous Advantage Actor Critic Network

The Asynchronous Advantage Actor Critic Network

The Asynchronous Advantage Actor Critic

Driving up a mountain with A3C

Summary

Questions

Further reading

Policy Gradients and Optimization

Policy Gradients and Optimization

Policy gradient

Deep deterministic policy gradient

Trust Region Policy Optimization

Proximal Policy Optimization

Summary

Questions

Further reading

Capstone Project – Car Racing Using DQN

Capstone Project – Car Racing Using DQN

Environment wrapper functions

Dueling network

Replay memory

Training the network

Car racing

Summary

Questions

Further reading

Recent Advancements and Next Steps

Recent Advancements and Next Steps

Imagination augmented agents

Learning from human preference

Deep Q learning from demonstrations

Hindsight experience replay

Hierarchical reinforcement learning

Inverse reinforcement learning

Summary

Questions

Further reading

Assessments

Assessments

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

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Summary

In this chapter, we learned how DRQN is used to remember information about the previous states and how it overcomes the problem of partially observable MDP. We have seen how to train our agent to play the game Doom using a DRQN algorithm. We have also learned about DARQN as an improvement to DRQN, which adds an attention layer on top of the convolution layer. Following this, we saw the two types of attention mechanism; namely, soft and hard attention.

In the next chapter, Chapter 10, Asynchronous Advantage Actor Critic Network, we will learn about another interesting deep reinforcement learning algorithm called Asynchronous Advantage Actor Critic network.

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