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

Inhaltsverzeichnis (Table of Contents)

• 1 Introduction
  • 1.1 Outline
• 2 Basics of MDPs and RL
  • 2.1 Markov Decision Processes
    • 2.1.1 Markov Process
  • 2.2 Value Function
  • 2.3 Policy Iteration
  • 2.4 Reinforcement Learning
    • 2.4.1 Monte Carlo Learning
    • 2.4.2 Temporal Difference Learning
• 3 Cleaning Robot Application
  • 3.1 Introduction
  • 3.2 Solving via Value Iteration
  • 3.3 Solving via Monte Carlo Learning
  • 3.4 Solving via Q-Learning
  • 3.5 Comparison of Results
• 4 Discussion

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This bachelor thesis aims to provide an understanding of Markov Decision Processes (MDPs) and present fundamental methods of Reinforcement Learning (RL), specifically Monte Carlo Learning and Q-Learning. The focus is on illustrating how these methods can be applied to solve decision problems modeled by MDPs. The work utilizes a cleaning robot application to demonstrate the practical implementation of these techniques.

• Markov Decision Processes (MDPs) and their role in decision-making
• Reinforcement Learning (RL) as a solution approach for MDP-based problems
• Exploring specific RL methods, including Monte Carlo Learning and Q-Learning
• Application of RL methods to a practical example: a cleaning robot
• Comparison and analysis of the results obtained using different RL methods

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter 1: Introduction provides a brief outline of the thesis's scope and structure.
• Chapter 2: Basics of MDPs and RL introduces the concept of Markov Decision Processes, including Markov Processes and the value function. It then explores policy iteration and delves into the fundamental principles of Reinforcement Learning, specifically focusing on Monte Carlo Learning and Temporal Difference Learning.
• Chapter 3: Cleaning Robot Application presents a practical application of the learned concepts. It introduces the cleaning robot problem and demonstrates how to solve it using Value Iteration, Monte Carlo Learning, and Q-Learning. This chapter concludes with a comparison of the results obtained using different methods.

Schlüsselwörter (Keywords)

This thesis focuses on Markov Decision Processes, Reinforcement Learning, Monte Carlo Learning, Q-Learning, Value Iteration, Cleaning Robot, Decision Problems, Optimal Policy, and Application. These keywords represent the core concepts and research focus of the work.