Reward Design Without the Expert: Testing LLM Assistance for Multi-Agent RL in Electricity Markets

Forschungsthema/Bereich

Reinforcement Learning · Large Language Models · Empirical IS Research

Typ der Abschlussarbeit

Bachelor / Master

Startzeitpunkt

-

Bewerbungsschluss

31.08.2026

Dauer der Arbeit

ab sofort

Beschreibung

Reward Design Without the Expert: Testing LLM Assistance for Multi-Agent RL in Electricity Markets

Background

Reinforcement learning is increasingly used to simulate strategic bidding in electricity markets, to test market designs before deployment or to study actors under new regulation. The bottleneck is reward design: it requires both deep market knowledge and RL experience, a rare combination that limits who can use Multi-Agent Reinforcement Learning (MARL) for market analysis today.

LLMs can generate reward functions from natural-language descriptions (Kwon 2023; Eureka; Text2Reward; REvolve 2024). But existing evaluations cover only robotics and game environments with technical metrics. Whether LLM support can substitute for or complement domain expertise, and whether it works in electricity-market MARL, has not been empirically tested.

What You Will Do

You will work directly with ASSUME, an open-source MARL framework for agent-based electricity-market simulation, and build a reward specification system that translates natural-language inputs into executable reward functions for heterogeneous market agents. The prototype is then evaluated in a user study comparing experts and non-experts in reward design tasks.

Expected Contributions

Methodological: First systematic test of whether LLM support reduces the expertise requirement in MARL reward design.

Technical: A working prototype for stakeholder-specifiable reward design on ASSUME.

Empirical: Quantitative and qualitative evidence on democratization effects in human–LLM collaboration for complex design tasks.

Disclaimer:The thesis is meant to teach you the scientific approach. Please keep that in mind. We expect you to learn and use scientific methods and to submit a scientifically sound result. Our role is to provide you with everything you need to succeed at that. We expect that you create a piece of work, in which you demonstrate a sound data collection method, an extensive dataset and your analysis of that dataset. A motivated student who works consistently on the thesis throughout the designated period can expect a very good grade. Details on the process and the format that we use to support you in succeeding with your thesis can be found here:
Bachelor: https://bwsyncandshare.kit.edu/s/g7pKCiScRZY5YtH
Master: https://bwsyncandshare.kit.edu/s/MiN8yTkfgeCrnSW

Voraussetzung

Voraussetzungen an Studierende

• Solid knowledge of Python and machine learning; ideally prior experience with reinforcement learning
• Willingness to get up to speed on electricity market fundamentals
• Interest in empirical user research
• Initiative in recruiting study participants

Studiengangsbereiche

• Wirtschafts- und Rechtswissenschaften
  Wirtschaftsinformatik

Betreuung

Titel, Vorname, Name

Julius Vincent Grams

Organisationseinheit

KIT WIN - ESIS

E-Mail Adresse

julius.grams@kit.edu

Link zur eigenen Homepage/Personenseite

Website

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E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an julius.grams@kit.edu


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