Hohei Chan

Hohei Chan

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

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publications

Ad Hoc Teamwork via Offline Goal-Based Decision Transformers

Xinzhi Zhang, Hohei Chan, Deheng Ye, Yang Cai, and Mengchen Zhao

ICML Proceedings of the 42nd International Conference on Machine Learning

PDF 
@inproceedings{zhang2025ad,
  title={Ad Hoc Teamwork via Offline Goal-Based Decision Transformers},
  author={Zhang, Xinzhi and Chan, Hohei and Ye, Deheng and Cai, Yi and Zhao, Mengchen},
  booktitle={Forty-second International Conference on Machine Learning},
  year={2025}
}

The ability of agents to collaborate with previously unknown teammates on the fly, known as ad hoc teamwork (AHT), is crucial in many real-world applications. Existing approaches to AHT require online interactions with the environment and some carefully designed teammates. However, these prerequisites can be infeasible in practice. In this work, we extend the AHT problem to the offline setting, where the policy of the ego agent is directly learned from a multi-agent interaction dataset. We propose a hierarchical sequence modeling framework called TAGET that addresses critical challenges in the offline setting, including limited data, partial observability and online adaptation. The core idea of TAGET is to dynamically predict teammate-aware rewards-to-go and sub-goals, so that the ego agent can adapt to the changes of teammates’ behaviors in real time. Extensive experimental results show that TAGET significantly outperforms existing solutions to AHT in the offline setting.

PADiff: Predictive and Adaptive Diffusion Policies for Ad Hoc Teamwork

Hohei Chan, Xinzhi Zhang, Antao Xiang, Weinan Zhang, and Mengchen Zhao

AAAI Proceedings of the AAAI Conference on Artificial Intelligence

PDF 
@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.

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