arXiv Artificial Intelligence

SRMT: Shared Memory for Multi-agent Lifelong Pathfinding

16 days ago 25.08.2026 07:00 👁 3 views
SRMT: Shared Memory for Multi-agent Lifelong Pathfinding

Quick summary

arXiv:2501.13200v2 Announce Type: replace-cross Abstract: Coordination in decentralized multi-agent reinforcement learning (MARL) necessitates that agents share information about their behavior and intentions. Existing approaches rely on communication protocols with domain or resource constraints or centralized training that poorly scales to large agent populations. We introduce the Shared Recurrent Memory Transformer (SRMT), which enables coordination through unconstrained communication. SRMT provides a global memory workspace where agents broadcast their learned working memory states and que

Key takeaways

  • arXiv:2501.13200v2 Announce Type: replace-cross Abstract: Coordination in decentralized multi-agent reinforcement learning (MARL) necessitates that agents share information about their behavior and intentions.
  • Existing approaches rely on communication protocols with domain or resource constraints or centralized training that poorly scales to large agent populations.
  • We introduce the Shared Recurrent Memory Transformer (SRMT), which enables coordination through unconstrained communication.

Why it matters

The importance of “SRMT: Shared Memory for Multi-agent Lifelong Pathfinding” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗