aiqbench analysis
PADiff addresses the critical challenge of ad hoc teamwork by leveraging diffusion models to capture multimodal cooperation strategies rather than collapsing into single behaviors. By integrating predictive information directly into the denoising process, this approach enables agents to adapt dynamically to unseen teammates in non-stationary environments. The method demonstrates significant performance gains across multiple cooperation benchmarks, offering a robust solution for autonomous multi-agent systems.
PADiff: Predictive and Adaptive Diffusion Policies for Ad Hoc Teamwork
arXiv:2511.07260v3 Announce Type: replace-cross Abstract: Ad hoc teamwork (AHT) requires agents to collaborate with previously unseen teammates, which is crucial for many real-world applications. The core challenge of AHT is to develop an ego agent that can predict and adapt to unknown teammates on the fly. Conventional RL-based approaches optimize a single expected return, which often causes policies to collapse into a single dominant behavior, thus failing to capture the multimodal cooperation patterns inherent in AHT. In this work, we introduce PADiff, a diffusion-based approach that captures agent's multimodal behaviors, unlocking its diverse cooperation modes with teammates. However, standard diffusion models lack the ability to predict and adapt in highly non-stationary AHT scenarios. To address this limitation, we propose a novel diffusion-based policy that integrates critical predictive information about teammates into the denoising process. Extensive experiments across three cooperation environments demonstrate that PADiff outperforms existing AHT methods significantly.
Abstract:Ad hoc teamwork (AHT) requires agents to collaborate with previously unseen teammates, which is crucial for many real-world applications. The core challenge of AHT is to develop an ego agent that can predict and adapt to unknown teammates on the fly. Conventional RL-based approaches optimize a single expected return, which often causes policies to collapse into a single dominant behavior, thus failing to capture the multimodal cooperation patterns inherent in AHT. In this work, we introduce PADiff, a diffusion-based approach that captures agent's multimodal behaviors, unlocking its diverse cooperation modes with teammates. However, standard diffusion models lack the ability to predict and adapt in highly non-stationary AHT scenarios. To address this limitation, we propose a novel diffusion-based policy that integrates critical predictive information about teammates into the denoising process. Extensive experiments across three cooperation environments demonstrate that PADiff outperforms existing AHT methods significantly.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2511.07260 [cs.AI]
(or arXiv:2511.07260v3 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2511.07260 Focus to learn more arXiv-issued DOI via DataCite
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