Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies
arXiv:2602.18291v1 Announce Type: new 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
arXiv:2602.18291v1 Announce Type: new 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.
Executive Summary
The article proposes a novel online multi-agent reinforcement learning framework, OMAD, which leverages diffusion policies to achieve efficient coordination among agents. By relaxing the policy objective and utilizing a joint distributional value function, OMAD overcomes the challenges of intractable likelihoods in diffusion models, resulting in a 2.5 to 5 times improvement in sample efficiency across various tasks.
Key Points
- ▸ Introduction of OMAD, a novel online multi-agent reinforcement learning framework
- ▸ Utilization of diffusion policies to enhance policy expressiveness
- ▸ Relaxed policy objective to facilitate effective exploration without relying on tractable likelihood
Merits
Improved Sample Efficiency
OMAD demonstrates a significant improvement in sample efficiency, outperforming existing methods by 2.5 to 5 times
Enhanced Policy Expressiveness
The use of diffusion policies enables more expressive and flexible agent behaviors
Demerits
Computational Complexity
The relaxed policy objective and joint distributional value function may introduce additional computational complexity
Limited Theoretical Analysis
The article lacks a comprehensive theoretical analysis of the proposed framework
Expert Commentary
The proposed OMAD framework is a significant contribution to the field of online multi-agent reinforcement learning. By addressing the challenges of intractable likelihoods in diffusion models, OMAD enables more efficient and effective coordination among agents. The relaxed policy objective and joint distributional value function are key innovations that facilitate this improvement. However, further research is needed to fully understand the theoretical implications of this framework and to explore its potential applications in real-world scenarios.
Recommendations
- ✓ Further theoretical analysis of the OMAD framework to provide a deeper understanding of its properties and limitations
- ✓ Exploration of the potential applications of OMAD in real-world scenarios, such as autonomous vehicles or robot swarms