Entwicklung eines Reinforcement Learning basierten Ansatzes zur Strategieoptimierung in einem Multi Agenten Roboter Szenario

A reinforcement‑learning approach based on Deep Q‑Learning is developed to optimise strategies for competing robots in a multi‑agent Microservice Dungeon scenario. Through a series of experiments, the agent’s performance is shown to improve with tailored reward functions, hyper‑parameter tuning, and observation adjustments, while the simplified environment and limited action space constrain its capabilities. The findings are highlighted as evidence that more advanced policy‑based methods and graph neural networks are needed to overcome these limitations and further enhance strategic decision‑making.