Before revision:  By exploiting global-local maps of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large  state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints  .
After revision:  By exploiting global-local maps of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints . Finally, learning a control policy that generalizes over the scenario parameter space enables us to analyze the influence of individual parameters on collection performance and provide some intuition about system-level benefits .