  Near future air taxi operations with electric vertical take-off and landing
(eVTOL) aircraft will be constrained by the need for frequent recharging of
eVTOLs, limited takeoff and landing pads in vertiports, and subject to
time-varying demand and electricity prices, making the eVTOL dispatch problem
unique and particularly challenging to solve. Previously, we have developed
optimization models to address this problem. Such optimization models however
suffer from prohibitively high computational run times when the scale of the
problem increases, making them less practical for real world implementation. To
overcome this issue, we have developed two deep reinforcement learning-based
eVTOL dispatch algorithms, namely single-agent and multi-agent deep Q-learning
eVTOL dispatch algorithms, where the objective is to maximize operating profit.
An eVTOL-based passenger transportation simulation environment was built to
assess the performance of our algorithms across $36$ numerical cases with
varying number of eVTOLs, vertiports, and demand. The results indicate that the
multi-agent eVTOL dispatch algorithm can closely approximate the optimal
dispatch policy with significantly less computational expenses compared to the
benchmark optimization model. The multi-agent algorithm was found to outperform
the single-agent counterpart with respect to both profits generated and
training time.
