Practical Reinforcement Learning

Master different reinforcement learning techniques and their practical implementation using OpenAI Gym, Python and Java
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Practical Reinforcement Learning

Dr. Engr. S.M. Farrukh Akhtar

3 customer reviews
Master different reinforcement learning techniques and their practical implementation using OpenAI Gym, Python and Java
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Book Details

ISBN 139781787128729
Paperback336 pages

Book Description

Reinforcement learning (RL) is becoming a popular tool for constructing autonomous systems that can improve themselves with experience. We will break the RL framework into its core building blocks, and provide you with details of each element.

This book aims to strengthen your machine learning skills by acquainting you with reinforcement learning algorithms and techniques. This book is divided into three parts. The first part defines Reinforcement Learning and describes its basics. It also covers the basics of Python and Java frameworks, which we are going to use later in the book. The second part discusses learning techniques with basic algorithms such as Temporal Difference, Monte Carlo, and Policy Gradient—all with practical examples. Lastly, in the third part we apply Reinforcement Learning with the most recent and widely used algorithms via practical applications.

By the end of this book, you'll know the practical implementation of case studies and current research activities to help you advance further with Reinforcement Learning.

Table of Contents

Chapter 1: Reinforcement Learning
Overview of machine learning
Introduction to reinforcement learning
Exploration versus exploitation
Neural network and reinforcement learning
Reinforcement learning frameworks/toolkits
Summary
Chapter 2: Markov Decision Process
Introduction to MDP
Bellman equation
A practical example of building an MDP domain
Markov chain
Building an object-oriented MDP domain
Summary
Chapter 3: Dynamic Programming
Learning and planning
Evaluating a policy
Value iteration
Policy iteration
Bellman equations
Summary
Chapter 4: Temporal Difference Learning
Introducing TD learning
TD lambda
Estimating from data
Learning rate
Overview of TD(1)
Overview of TD(0)
TD lambda rule
K-step estimator
Relationship between k-step estimators and TD lambda
Summary
Chapter 5: Monte Carlo Methods
Monte Carlo methods
Monte Carlo for control
Summary
Chapter 6: Learning and Planning
Q-learning
Summary
Chapter 7: Deep Reinforcement Learning
What is a neural network?
Deep learning
Deep Q Network
Summary
Chapter 8: Game Theory
Introduction to game theory
OpenAI Gym examples
Summary
Chapter 9: Reinforcement Learning Showdown
Reinforcement learning frameworks
Summary
Chapter 10: Applications and Case Studies – Reinforcement Learning
Inverse Reinforcement Learning
Partially Observable Markov Decision Process
Summary
Chapter 11: Current Research – Reinforcement Learning
Hierarchical reinforcement learning
Reinforcement learning with hierarchies of abstract machines
MAXQ value function decomposition
Summary

What You Will Learn

  • Understand basics of reinforcement learning methods, algorithms, and more, and the differences between supervised, unsupervised, and reinforcement learning
  •  Master the Markov Decision Process math framework by building an OO-MDP Domain in Java
  • Learn dynamic programming principles and the implementation of Fibonacci computation in Java
  • Understand Python implementation of temporal difference learning
  • Develop Monte Carlo methods and various policies used to build a Monte Carlo simulator using Python
  • Understand Policy Gradient methods and policies applied in the reinforcement domain
  • Instill reinforcement methods in the autonomous platform using a moving car example
  • Apply reinforcement learning algorithms in games with REINFORCEjs

Authors

Table of Contents

Chapter 1: Reinforcement Learning
Overview of machine learning
Introduction to reinforcement learning
Exploration versus exploitation
Neural network and reinforcement learning
Reinforcement learning frameworks/toolkits
Summary
Chapter 2: Markov Decision Process
Introduction to MDP
Bellman equation
A practical example of building an MDP domain
Markov chain
Building an object-oriented MDP domain
Summary
Chapter 3: Dynamic Programming
Learning and planning
Evaluating a policy
Value iteration
Policy iteration
Bellman equations
Summary
Chapter 4: Temporal Difference Learning
Introducing TD learning
TD lambda
Estimating from data
Learning rate
Overview of TD(1)
Overview of TD(0)
TD lambda rule
K-step estimator
Relationship between k-step estimators and TD lambda
Summary
Chapter 5: Monte Carlo Methods
Monte Carlo methods
Monte Carlo for control
Summary
Chapter 6: Learning and Planning
Q-learning
Summary
Chapter 7: Deep Reinforcement Learning
What is a neural network?
Deep learning
Deep Q Network
Summary
Chapter 8: Game Theory
Introduction to game theory
OpenAI Gym examples
Summary
Chapter 9: Reinforcement Learning Showdown
Reinforcement learning frameworks
Summary
Chapter 10: Applications and Case Studies – Reinforcement Learning
Inverse Reinforcement Learning
Partially Observable Markov Decision Process
Summary
Chapter 11: Current Research – Reinforcement Learning
Hierarchical reinforcement learning
Reinforcement learning with hierarchies of abstract machines
MAXQ value function decomposition
Summary

Book Details

ISBN 139781787128729
Paperback336 pages
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