Despite their groundbreaking performance, autonomous agents can misbehave when training and environmental conditions become inconsistent, with minor mismatches leading to undesirable behaviors or even catastrophic failures. Robustness towards these training-environment ambiguities is a core requirement for intelligent agents and its fulfillment is a long-standing challenge towards their real-world deployments. Here, we introduce a Distributionally Robust Free Energy model (DR-FREE) that instills this core property by design. Combining a robust extension of the free energy principle with a resolution engine, DR-FREE wires robustness into the agent decision-making mechanisms. Across benchmark experiments, DR-FREE enables the agents to complete the task even when, in contrast, state-of-the-art models fail. This milestone may inspire both deployments in multi-agent settings and, at a perhaps deeper level, the quest for an explanation of how natural agents -- with little or no training -- survive in capricious environments.

翻译：尽管自主智能体在性能上取得了突破性进展，但当训练条件与环境条件不一致时，它们可能出现行为异常，微小的失配即可导致不良行为甚至灾难性故障。对训练-环境模糊性的鲁棒性是智能体的核心需求，实现这一需求是其实际部署中长期面临的挑战。本文提出了一种分布鲁棒自由能模型（DR-FREE），该模型通过设计内嵌了这一核心特性。DR-FREE将自由能原理的鲁棒性扩展与求解引擎相结合，将鲁棒性融入智能体的决策机制中。在基准实验中，DR-FREE使智能体能够在任务中持续运作，而相比之下，现有最先进模型则会失败。这一里程碑成果可能激发多智能体场景中的实际部署，并在更深的层面上，推动对自然智能体——在极少或无需训练的情况下——如何在多变环境中存续的解释性探索。