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Modeling the driving behavior of traffic partici-pants in highly interactive traffic situations, such as roundabouts, poses a significant challenge due to the complex interactions and the variety of traffic situations. To address this task, we propose a combination of graph-based representations of the environment with Multi-Agent Reinforcement Learning (MARL). By utilizing a graph-based representation of the local environment of each vehicle, our approach efficiently accounts for road structures and a varying number of surrounding vehicles interacting with each other. Building upon this representation, MARL enables us to learn a driving policy based on a minimal set of principles: drivers want to move along the road while avoiding collisions and maintaining comfortable accelerations. Sharing the learned policy among all agents allows us to leverage Proximal Policy Optimization (PPO), a policy gradient Reinforcement …