  Electric vertical-takeoff and landing (eVTOL) aircraft, recognized for their
maneuverability and flexibility, offer a promising alternative to our
transportation system. However, the operational effectiveness of these aircraft
faces many challenges, such as the delicate balance between energy and time
efficiency, stemming from unpredictable environmental factors, including wind
fields. Mathematical modeling-based approaches have been adopted to plan
aircraft flight path in urban wind fields with the goal to save energy and time
costs. While effective, they are limited in adapting to dynamic and complex
environments. To optimize energy and time efficiency in eVTOL's flight through
dynamic wind fields, we introduce a novel path planning method leveraging deep
reinforcement learning. We assess our method with extensive experiments,
comparing it to Dijkstra's algorithm -- the theoretically optimal approach for
determining shortest paths in a weighted graph, where weights represent either
energy or time cost. The results show that our method achieves a graceful
balance between energy and time efficiency, closely resembling the
theoretically optimal values for both objectives.
