Scaling Multi-Agent Environment Co-Design with Diffusion Models

• URL: http://arxiv.org/abs/2511.03100v1
• Date: Wed, 05 Nov 2025 01:09:40 GMT
• Title: Scaling Multi-Agent Environment Co-Design with Diffusion Models
• Abstract summary: Agent-environment co-design aims to jointly optimise agent policies and environment configurations in search of improved system performance.<n>Current co-design methods struggle to scale, collapse under high-dimensional environment design spaces and suffer from sample inefficiency.<n>We develop a scalable and sample-efficient co-design framework pushing co-design towards practically relevant settings.<n>Our method consistently exceeds the state-of-the-art, achieving 39% higher rewards in the warehouse setting with 66% fewer simulation samples.
• Score: 15.05916455401006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 challenges by developing Diffusion Co-Design (DiCoDe), a scalable and sample-efficient co-design framework pushing co-design towards practically relevant settings. DiCoDe incorporates two core innovations. First, we introduce Projected Universal Guidance (PUG), a sampling technique that enables DiCoDe to explore a distribution of reward-maximising environments while satisfying hard constraints such as spatial separation between obstacles. Second, we devise a critic distillation mechanism to share knowledge from the reinforcement learning critic, ensuring that the guided diffusion model adapts to evolving agent policies using a dense and up-to-date learning signal. Together, these improvements lead to superior environment-policy pairs when validated on challenging multi-agent environment co-design benchmarks including warehouse automation, multi-agent pathfinding and wind farm optimisation. Our method consistently exceeds the state-of-the-art, achieving, for example, 39% higher rewards in the warehouse setting with 66% fewer simulation samples. This sets a new standard in agent-environment co-design, and is a stepping stone towards reaping the rewards of co-design in real world domains.

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