Multi-agent RL policies for heterogeneous sUAS fleets reach equilibria for safe separation in package delivery simulations, outperforming some rule-based baselines but favoring stronger configurations.
Integrated conflict management for UAM with strategic demand capacity balancing and learning-based tactical deconfliction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.MA 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning
Multi-agent RL policies for heterogeneous sUAS fleets reach equilibria for safe separation in package delivery simulations, outperforming some rule-based baselines but favoring stronger configurations.