  Reinforcement learning-based path planning for multi-agent systems of varying
size constitutes a research topic with increasing significance as progress in
domains such as urban air mobility and autonomous aerial vehicles continues.
Reinforcement learning with continuous state and action spaces is used to train
a policy network that accommodates desirable path planning behaviors and can be
used for time-critical applications. A Long Short-Term Memory module is
proposed to encode an unspecified number of states for a varying, indefinite
number of agents. The described training strategies and policy architecture
lead to a guidance that scales to an infinite number of agents and unlimited
physical dimensions, although training takes place at a smaller scale. The
guidance is implemented on a low-cost, off-the-shelf onboard computer. The
feasibility of the proposed approach is validated by presenting flight test
results of up to four drones, autonomously navigating collision-free in a
real-world environment.
