Autonomous mobility is emerging as a new mode of urban transportation for
moving cargo and passengers. However, such fleet coordination schemes face
significant challenges in scaling to accommodate fast-growing fleet sizes that
vary in their operational range, capacity, and communication capabilities. We
introduce the concept of partially observable advanced air mobility games to
coordinate a fleet of aerial vehicle agents accounting for their heterogeneity
and self-interest inherent to commercial mobility fleets. We propose a novel
heterogeneous graph attention-based encoder-decoder (HetGAT Enc-Dec) neural
network to construct a generalizable stochastic policy stemming from the inter-
and intra-agent relations within the mobility system. We train our policy by
leveraging deep multi-agent reinforcement learning, allowing decentralized
decision-making for the agents using their local observations. Through
extensive experimentation, we show that the fleets operating under the HetGAT
Enc-Dec policy outperform other state-of-the-art graph neural network-based
policies by achieving the highest fleet reward and fulfillment ratios in an
on-demand mobility network.

介绍了部分可观察的先进空中移动游戏的概念，提出了一种新的异构图注意力编码器 - 解码器 (HetGAT Enc-Dec) 神经网络，以构建可推广的随机策略来协调机动车辆代理，此政策通过深度一致性学习进行训练，使代理人能够使用其本地观察进行分散的决策制定。通过大量实验表明，采用 HetGAT Enc-Dec 政策的车队在按需移动网络中表现最佳。