Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs. We propose Master-Agent Proto-plan System (MAPS), a hierarchical deep reinforcement learning (DRL) architecture in which a centralized Master agent generates a compact, continuous embedding, denoted as proto-plan, that encodes a global coordination strategy. Decentralized Worker agents integrate this embedding with local observation
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- PossiblePossibly related (embedding) · 52%Agentic AI for Robot Teams →
- PossiblePossibly related (embedding) · 50%Autonomous navigation of intelligent microrobotic swarms in unknown environments →
- LinkedLinked via arxiv author · 85%Gil Lifshits →
“Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections”
- LinkedLinked via arxiv author · 85%Igal Bilik →
“Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections”
- LinkedLinked via arxiv author · 85%Gilad Katz →
“Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections”