Modeling other drivers' behavior in highly interactive traffic situations, such as roundabouts, is a challenging task. We address this task using a Multi-Agent Reinforcement Learning (MARL) approach that learns a driving policy based on a minimal set of assumptions: drivers want to move forward and avoid collisions while maintaining low accelerations. Each agent's actions depend only on his observation of the local environment; no explicit communication between agents is possible. In order to teach the agents to safely interact with each other, and for example, respect right-of-way rules, we use parameter sharing: During training all vehicles are controlled by the same policy and the aggregated experiences are used to improve the policy. Moreover, parameter sharing enables us to use the efficient Soft Actor Critic (SAC) algorithm for training. The approach is evaluated in a roundabout setting with different traffic densities. Furthermore, the ability of the model to generalize is assessed in an untrained roundabout. In both settings, success rates above 97 % demonstrate that a safe and transferable policy is learned.