arXiv:2511.03100v2 Announce Type: replace Abstract: The agent-environment co-design paradigm jointly optimises agent policies and environment configurations in search of improved system performance. With application domains ranging from warehouse logistics to windfarm management, co-design promises to fundamentally change how we deploy multi-agent systems. However, current co-design methods struggle to scale. They collapse under high-dimensional environment design spaces and suffer from sample inefficiency when addressing moving targets inherent to joint optimisation. We address these challeng
The proliferation of complex multi-agent systems demands more efficient co-design methodologies, and advancements in diffusion models offer a pathway to overcome current scaling limitations.
Improved multi-agent environment co-design can significantly enhance the efficiency and performance of complex systems across various critical sectors, from logistics to energy management.
This research introduces diffusion models to address the scalability and sample inefficiency challenges in co-design, potentially enabling the deployment of more sophisticated and adaptable multi-agent AI systems.
- · AI agents developers
- · Logistics industry
- · Renewable energy sector
- · Academia (AI/ML)
- · Legacy co-design methods
- · Inefficient multi-agent system deployments
More robust and efficient multi-agent systems become deployable in real-world, high-dimensional environments.
This could accelerate the adoption of autonomous AI agents in new and more complex operational domains.
Enhanced multi-agent system performance could drive productivity gains and further automation across key industries, impacting labor markets and resource allocation.
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Read at arXiv cs.LG