  Harvesting data from distributed Internet of Things (IoT) devices with
multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem
requiring flexible path planning methods. We propose a multi-agent
reinforcement learning (MARL) approach that, in contrast to previous work, can
adapt to profound changes in the scenario parameters defining the data
harvesting mission, such as the number of deployed UAVs, number, position and
data amount of IoT devices, or the maximum flying time, without the need to
perform expensive recomputations or relearn control policies. We formulate the
path planning problem for a cooperative, non-communicating, and homogeneous
team of UAVs tasked with maximizing collected data from distributed IoT sensor
nodes subject to flying time and collision avoidance constraints. The path
planning problem is translated into a decentralized partially observable Markov
decision process (Dec-POMDP), which we solve through a deep reinforcement
learning (DRL) approach, approximating the optimal UAV control policy without
prior knowledge of the challenging wireless channel characteristics in dense
urban environments. By exploiting a combination of centered global and local
map representations 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 complex environments and 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.
