Framework introduction – Bachelor's theses

As is the custom at the HPI, each of the six original students that worked on the project wrote their respective bachelor's thesis on a topic related to the project work.

All of these theses offer in-depth information of a specific topic related to the project.

The Marketplace of the Future: Simulation of Market Processes in Re-Commerce

By: Nick Bessin (@SinNeax)
Language: German
German Title: Der Marktplatz der Zukunft: Simulation von Marktprozessen im Re-Commerce

Abstract:
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Links:
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Pricing in the Re-Commerce Domain: Analysis of Pricing Strategies with an Online Market Simulation

By: Leonard Dreeßen (@ldreessen)
Language: German
German Title: Preisfindung in der Recommerce-Domäne: Analyse von Preisstrategien mithilfe einer Online-Marktsimulation

Abstract:
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Links:
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A comparison of reinforcement learning algorithms for dynamic pricing in recommerce markets

By: Jan Niklas Groeneveld (@jannikgro)
Language: German
German Title: Ein Vergleich von Reinforcement-Learning-Algorithmen für die dynamische Preisgestaltung auf Recommerce-Märkten

Abstract:
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Links:
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Scalable Learning in the Cloud

By: Judith Herrmann (@felix-20)
Language: German
German Title: Skalierbares Lernen in der Cloud

Abstract (German):
Cloud Umgebungen gewinnen immer mehr an Bedeutung. Gerade im Bereich des Machine Learnings bieten sie die Möglichkeit den Lernprozess effizienter zu gestalten. Die bessere Hardware, die einfache und andauernde Verfügbarkeit macht diese entfernten Rechner für moderne Systeme unverzichtbar. Von großen Anbietern wird oft eine Komplettlösung verkauft, die alle Werkzeuge beinhaltet, die für Forschende zum Arbeiten notwendig sind. Dabei sind jedoch nur einige wenige auf Reinforcement Learning spezialisiert. In dieser Arbeit wird eine Cloud Architektur vorgestellt, mit der Reinforcement Learning Agenten trainiert werden können. Sie basiert auf einer Marktsimulation, in der diese eine möglichst optimale Preisstrategien lernen sollen. Die Anforderungen und die Umsetzungen werden thematisiert. Außerdem wird die Skalierbarkeit der Architektur anhand durchgeführter Experimente mit der Marktsimulation gezeigt werden.

Links:
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Monitoring of Agents for Dynamic Pricing in different Recommerce Markets

By: Nikkel Mollenhauer (@NikkelM)
Language: English

Abstract:
Sustainable recommerce markets are growing faster than ever. In such markets, customers are incentivised to resell their used products to businesses, which then refurbish and sell them again on the secondary market. However, businesses now face the challenge of having to set three different prices for the same item: One price for the new item, one for its refurbished version and the price at which items are bought back from customers. Since these prices are heavily influenced by each other, traditional pricing methods become less effective. To solve this dynamic pricing problem, a simulation framework was built which can be used to train artificial vendors to set optimised prices using Reinforcement learning algorithms. Before employing these trained agents on real markets, their fitness must be monitored and evaluated, as prices that are too high or too low can lead to high costs for the business. This thesis introduces a number of ways that such dynamic pricing agents can be monitored. We come to the conclusion that it is best to use a wide range of tools when evaluating different aspects of an agent’s performance, from running large-scale simulations to monitoring small policy changes following shifting market states.

Links:
Download link
Github repository

Pretraining RL-Based Pricing Agents for Recommerce Applications Using Historical market Data

By: Johann Schulze Tast (@blackjack2693)
Language: German
German Title: Vortrainieren RL-basierter Preisfindungsagenten für Recommerce mit geammelten Marktdaten

Abstract:
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Links:
to be inserted when complete