  Unmanned aerial vehicles (UAVs) are expected to be an integral part of
wireless networks, and determining collision-free trajectory in multi-UAV
non-cooperative scenarios while collecting data from distributed Internet of
Things (IoT) nodes is a challenging task. In this paper, we consider a path
planning optimization problem to maximize the collected data from multiple IoT
nodes under realistic constraints. The considered multi-UAV non-cooperative
scenarios involve random number of other UAVs in addition to the typical UAV,
and UAVs do not communicate or share information among each other. We translate
the problem into a Markov decision process (MDP) with parameterized states,
permissible actions, and detailed reward functions. Dueling double deep
Q-network (D3QN) is proposed to learn the decision making policy for the
typical UAV, without any prior knowledge of the environment (e.g., channel
propagation model and locations of the obstacles) and other UAVs (e.g., their
missions, movements, and policies). The proposed algorithm can adapt to various
missions in various scenarios, e.g., different numbers and positions of IoT
nodes, different amount of data to be collected, and different numbers and
positions of other UAVs.
  Numerical results demonstrate that real-time navigation can be efficiently
performed with high success rate, high data collection rate, and low collision
rate.
