让AI从观察中识别并重建其他智能体行为，实现协作
Artificial Empathy: Towards a Framework for Unsupervised Agency Detection and Policy Reconstruction
- 用独立任务训练的RL代理作为先验，探测环境中的智能体
- 可从观测中重构其他智能体的行为策略，无需标注数据
- 适合研究多智能体协作与自主系统行为理解
我们研究了人工智能系统如何仅通过观察就识别并建模环境中其他智能体的能力，这是实现在真实世界中合作行为所必需的。该问题比逆强化学习约束更少，但至今仍鲜有探索。本文提出一个框架，利用在独立任务上训练的强化学习代理作为关于智能体动态的先验知识，实现对其他智能体的检测与策略重建。
原文摘要 · Abstract (English)
We study how an AI system can identify and model other agents in its environment from observation alone, which is a capability necessary for cooperative behaviour in the real world. This problem is less constrained than inverse reinforcement learning and remains largely unexplored. We propose a framework that uses a reinforcement learning agent, trained on an independent task as a prior about agentic dynamics, to perform agency detection and policy reconstruction.
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