Learning Self-Orchestration in Decentralized Agent Swarms

Key points

• The capabilities of an agent swarm depend on both its individual members and how they coordinate.1
• Yet existing multi-agent systems often train agents within fixed orchestration structures, separating the learning of individual capabilities from the organization of collective behavior.1
• Results show gains over monolithic post-training and confirm that agent participation indeed evolves with learning.1
• With the same post-trained action policies, learned self-orchestration outperforms alternative coordination strategies.1

Sources (2)

• [1]Learning Self-Orchestration in Decentralized Agent Swarms
  Hugging Face Daily Papers · Oct 15, 12:00 AM

  We introduce market-based post-training (MPT), a framework for jointly learning action policies and self-orchestration in decentralized agent swarms.

  The capabilities of an agent swarm depend on both its individual members and how they coordinate.

• [2]Learning Self-Orchestration in Decentralized Agent Swarms
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:51 PM · same content

Extractive summary: sentences quoted from the sources.