English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Autonomous mobility is emerging as a new disruptive mode of urbantransportation for moving cargo and passengers. However, designing scalableautonomous fleet coordination schemes to accommodate fast-growing mobilitysystems is challenging primarily due to the increasing heterogeneity of thefleets, time-varying demand patterns, service area expansions, andcommunication limitations. We introduce the concept of partially observableadvanced air mobility games to coordinate a fleet of aerial vehicles byaccounting for the heterogeneity of the interacting agents and theself-interested nature inherent to commercial mobility fleets. To model thecomplex interactions among the agents and the observation uncertainty in themobility networks, we propose a novel heterogeneous graph attentionencoder-decoder (HetGAT Enc-Dec) neural network-based stochastic policy. Wetrain the policy by leveraging deep multi-agent reinforcement learning,allowing decentralized decision-making for the agents using their localobservations. Through extensive experimentation, we show that the learnedpolicy generalizes to various fleet compositions, demand patterns, andobservation topologies. Further, fleets operating under the HetGAT Enc-Decpolicy outperform other state-of-the-art graph neural network policies byachieving the highest fleet reward and fulfillment ratios in on-demand mobilitynetworks.

Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility