Efficient UAV Trajectory-Planning using Economic Reinforcement Learning
  Advances in unmanned aerial vehicle (UAV) design have opened up applications
as varied as surveillance, firefighting, cellular networks, and delivery
applications. Additionally, due to decreases in cost, systems employing fleets
of UAVs have become popular. The uniqueness of UAVs in systems creates a novel
set of trajectory or path planning and coordination problems. Environments
include many more points of interest (POIs) than UAVs, with obstacles and
no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement
learning algorithm inspired by economic transactions to distribute tasks
between UAVs. This system revolves around an economic theory, in particular an
auction mechanism where UAVs trade assigned POIs. We formulate the path
planning problem as a multi-agent economic game, where agents can cooperate and
compete for resources. We then translate the problem into a Partially
Observable Markov decision process (POMDP), which is solved using a
reinforcement learning (RL) model deployed on each agent. As the system
computes task distributions via UAV cooperation, it is highly resilient to any
change in the swarm size. Our proposed network and economic game architecture
can effectively coordinate the swarm as an emergent phenomenon while
maintaining the swarm's operation. Evaluation results prove that REPlanner
efficiently outperforms conventional RL-based trajectory search.
