用扩散模型让智能体预判并适应临时队友，实现多样协作。
PADiff: Predictive and Adaptive Diffusion Policies for Ad Hoc Teamwork
- 基于扩散模型捕捉多模式合作行为
- 在三个环境中显著超越现有方法
- 适合需要动态协作的复杂场景
临时团队协作（AHT）要求智能体与未曾见过的队友协同工作，这对许多现实应用至关重要。核心挑战在于训练一个能实时预测并适应未知队友的主体智能体。传统基于强化学习的方法优化单一期望回报，常导致策略坍缩为单一主导行为，无法捕捉AHT中固有的多模态协作模式。本文提出PADiff，一种基于扩散模型的方法，能够捕获智能体的多模态行为，解锁其与队友的多样化协作方式。然而，标准扩散模型缺乏在高度非平稳的AHT场景中进行预测与适应的能力。为此，我们提出一种新型扩散策略，将关于队友的关键预测信息融入去噪过程。在三个协作环境中的大量实验表明，PADiff显著优于现有AHT方法。
原文摘要 · Abstract (English)
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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