SAGA formulates UAV local planning as joint regression and ranking over motion anchors with self-attention and polar positional encoding, achieving 100% success rate at high speeds where baselines fail.
Accelerate multi-agent reinforcement learning in zero-sum games with subgame curriculum learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence
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SAGA: A Robust Self-Attention and Goal-Aware Anchor-based Planner for Safe UAV Autonomous Navigation
SAGA formulates UAV local planning as joint regression and ranking over motion anchors with self-attention and polar positional encoding, achieving 100% success rate at high speeds where baselines fail.