Methods of Machine Learning and their Application. The Basics of Markov Decision Processes and Reinforcement Learning

Omar Baiazid

ISBN 10: 3346518191 / ISBN 13: 9783346518194

Published by GRIN Verlag, 2021

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Bachelor Thesis from the year 2021 in the subject Engineering - Computer Engineering, grade: 1,3, Hamburg University of Technology (Embedded Systems), language: English, abstract: This bachelor thesis aims to illustrate the idea behind Markov Decision Processes (MDP) and to present a few basic methods of Reinforcement Learning (RL) namely Monte Carlo Learning and Q-Learning, which are the solutions for decision problems modelled by MDPs. For the last section we apply these methods on an application and in the end discuss the results. Let us imagine the scenario where we put a hamster inside a maze, we expect the hamster to go through the maze till it reaches some point we considered as the goal. Well, it may randomly work but most of the time it won¿t. At this place, the hamster does not know how important this particular point remains namely the goal. But how will it be, when we remunerate the hamster once the goal is reached, he receives a reward for example a piece of cheese. The hamster will start to remember the route, which leads to the cheese and he maybe will learn to go the easy and quick way to achieve this goal. What we did, is that we reinforce the good behavior of the hamster by giving it some reward.

Seller Inventory # 9783346518194

Bibliographic Details

• Title: Methods of Machine Learning and their Application. The Basics of Markov Decision Processes and Reinforcement Learning
• Publisher: GRIN Verlag
• Publication Date: 2021
• Language: English
• ISBN 10: 3346518191
• ISBN 13: 9783346518194
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 3D millimeters 210 millimeters width by 148 millimeters height by 3 millimeters depth
• Weight: 62 grams

About this title

Synopsis

Bachelor Thesis from the year 2021 in the subject Engineering - Computer Engineering, grade: 1,3, Hamburg University of Technology (Embedded Systems), language: English, abstract: This bachelor thesis aims to illustrate the idea behind Markov Decision Processes (MDP) and to present a few basic methods of Reinforcement Learning (RL) namely Monte Carlo Learning and Q-Learning, which are the solutions for decision problems modelled by MDPs. For the last section we apply these methods on an application and in the end discuss the results. Let us imagine the scenario where we put a hamster inside a maze, we expect the hamster to go through the maze till it reaches some point we considered as the goal. Well, it may randomly work but most of the time it won't. At this place, the hamster does not know how important this particular point remains namely the goal. But how will it be, when we remunerate the hamster once the goal is reached, he receives a reward for example a piece of cheese. The hamster will start to remember the route, which leads to the cheese and he maybe will learn to go the easy and quick way to achieve this goal. What we did, is that we reinforce the good behavior of the hamster by giving it some reward.