M-ROSE: A Multi Robot Simulation Environment for Learning Cooperative Behavior

The development of high-performance autonomous multi robot control systems requires intensive experimentation in controllable, repeatable, and realistic robot settings. The need for experimentation is even higher in applications where the robots should automatically learn substantial parts of their controllers. We propose to solve such learning tasks as a three step process. First, we learn a simulator of the robots' dynamics. Second, we perform the learning tasks using the learned simulator. Third, we port the learned controller to the real robot and cross validate the performance gains obtained by the learned controllers. In this paper, we describe M-ROSE, our learning simulator, and provide empirical evidence that it is a powerful tool for learning of sophisticated control modules for real robots