A delay-aware multi-agent RL framework for active visual triangulation shows that Age-of-Information metadata, perception-consistent rewards, and multi-source covariance propagation each improve cooperative target localization under realistic communication delays.
Rainbow delay compensation: A multi-agent reinforcement learning framework for mitigating observation delays,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Delay-Aware Active Triangulation with Uncertainty-Driven Multi-Agent Reinforcement Learning for Counter-UAS
A delay-aware multi-agent RL framework for active visual triangulation shows that Age-of-Information metadata, perception-consistent rewards, and multi-source covariance propagation each improve cooperative target localization under realistic communication delays.