Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies

• URL: http://arxiv.org/abs/2602.18291v1
• Date: Fri, 20 Feb 2026 15:38:02 GMT
• Title: Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies
• Authors: Zhuoran Li, Hai Zhong, Xun Wang, Qingxin Xia, Lihua Zhang, Longbo Huang,
• Abstract summary: Diffusion-based generative models are well-positioned to meet the needs of online Multi-Agent Reinforcement Learning (MARL)<n>We propose among the first underlineOnline off-policy underlineMARL framework using underlineDiffusion policies to orchestrate coordination.<n>Our key innovation is a relaxed policy objective that maximizes scaled joint entropy, facilitating effective exploration without relying on tractable likelihood.
• Score: 51.24079409973799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination. Crucially, enhancing policy expressiveness is pivotal for achieving superior performance. Diffusion-based generative models are well-positioned to meet this demand, having demonstrated remarkable expressiveness and multimodal representation in image generation and offline settings. Yet, their potential in online MARL remains largely under-explored. A major obstacle is that the intractable likelihoods of diffusion models impede entropy-based exploration and coordination. To tackle this challenge, we propose among the first \underline{O}nline off-policy \underline{MA}RL framework using \underline{D}iffusion policies (\textbf{OMAD}) to orchestrate coordination. Our key innovation is a relaxed policy objective that maximizes scaled joint entropy, facilitating effective exploration without relying on tractable likelihood. Complementing this, within the centralized training with decentralized execution (CTDE) paradigm, we employ a joint distributional value function to optimize decentralized diffusion policies. It leverages tractable entropy-augmented targets to guide the simultaneous updates of diffusion policies, thereby ensuring stable coordination. Extensive evaluations on MPE and MAMuJoCo establish our method as the new state-of-the-art across $10$ diverse tasks, demonstrating a remarkable $2.5\times$ to $5\times$ improvement in sample efficiency.

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