Hohei Chan

Hohei Chan

MS student at SCUT and intern with the Kling Team at Kuaishou. Research: reinforcement learning, agents, and multimodal generation.

PADiff: Predictive and Adaptive Diffusion Policies for Ad Hoc Teamwork

Published in Proceedings of the AAAI Conference on Artificial Intelligence, 2026

In offline multi-agent collaboration scenarios, we propose a diffusion-based decision framework that enables the modeling of various collaboration modes for unknown teammate agents.

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@inproceedings{chan2026padiff,
  title={PADiff: Predictive and Adaptive Diffusion Policies for Ad Hoc Teamwork},
  author={Chan, Hohei and Zhang, Xinzhi and Xiang, Antao and Zhang, Weinan and Zhao, Mengchen},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  number={24},
  pages={19943--19951},
  year={2026}
}

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.