GameChat enables multi-agent navigation via LLM dialogue, cutting travel time by 20-35% and ensuring priority agents reach goals first in simulations.
Dmca: Dense multi- agent navigation using attention and communication
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SCALE-COMM uses contrastive alignment on latent embeddings to decouple and stabilize communication learning from policy optimization in decentralized MARL, showing gains on benchmarks and a warehouse task.
citing papers explorer
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GameChat: Multi-LLM Dialogue for Safe, Agile, and Socially Optimal Multi-Agent Navigation in Constrained Environments
GameChat enables multi-agent navigation via LLM dialogue, cutting travel time by 20-35% and ensuring priority agents reach goals first in simulations.
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SCALE-COMM: Shared, Contrastively-Aligned Latent Embeddings for MARL Communication
SCALE-COMM uses contrastive alignment on latent embeddings to decouple and stabilize communication learning from policy optimization in decentralized MARL, showing gains on benchmarks and a warehouse task.