Book Image

The Reinforcement Learning Workshop

By : Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak
Book Image

The Reinforcement Learning Workshop

By: Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak

Overview of this book

Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, youÔÇÖll be guided through different RL environments and frameworks. YouÔÇÖll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youÔÇÖve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youÔÇÖll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, youÔÇÖll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, youÔÇÖll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.
Table of Contents (14 chapters)
Preface
Free Chapter
2
2. Markov Decision Processes and Bellman Equations

Introduction

In the previous chapter, we looked at various policy-based methods and their advantages. In this chapter, we are going to learn about gradient-free methods, namely genetic algorithms; develop these algorithms step by step; and use them to optimize neural networks and RL-based algorithms. This chapter discusses the limitations of gradient-based methods, such as getting stuck at local optima and slower convergence when dealing with noisy input. This chapter presents an alternative optimization solution to gradient methods through genetic algorithms, as they ensure global optimum convergence. You will examine and implement the structure of genetic algorithms and implement them through hyperparameter selection for neural networks and evolving network topologies, as well as using them in combination with RL for a cart-pole balancing activity. Hybrid neural networks that use genetic algorithms are used to solve complex problems, such as modeling plasma chemical reactors, designing...