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 learned that DQNs achieved higher performance compared to traditional reinforcement learning techniques. Video games are a perfect example of where DQN models excel. Training an agent to play video games can be quite difficult for traditional reinforcement learning agents as there is a huge number of possible combinations of states, actions, and Q-values to be processed and analyzed during the training.

Deep learning algorithms are renowned for handling high-dimensional tensors. Some researchers combined Q-learning techniques with deep learning models to overcome this limitation and came up with DQNs. A DQN model comprises a deep learning model that is used as a function approximation of Q-values. This technique constituted a major breakthrough in the reinforcement learning field as it helped to handle much larger state and action spaces than traditional models.

Since then, further research has been undertaken and different types of DQN...