世界模型的清醒梦：学习怀疑想象并依据信任决策
Lucid Dreaming for World Models: Learning to Doubt Imagination and Decide by Trust
- National University of Singapore（新加坡国立大学）
- King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
- Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出清醒世界模型，通过主观逻辑学习怀疑并传播信任，减少想象预测的不确定性，在导航任务中将步数从362降至190。
AI中文摘要：
世界模型使智能体能够在想象中学习和规划，但超出其经验的预测可能变得不可靠并误导决策。现有的从预测中得出的不确定性估计在面对不熟悉的状态-动作对时可能仍然过度自信。我们提出了清醒世界模型（LucidWM），它从经验中学习怀疑，并通过想象传播信任。通过将主观逻辑整合到分类潜在状态转移中，LucidWM区分了预测结果与其证据支持，并为每个转移分配一个怀疑程度。该怀疑的补数定义了转移级别的信任，该信任沿想象轨迹乘性累积，以重新加权回报用于策略学习并指导动作选择。不确定性估计不需要额外的参数或前向传递。在四个基础世界模型上针对十七种不确定性读数进行评估，LucidWM在动作损坏的滚动过程中检测环境变化并发出不确定性信号。在一个受控导航案例研究中，基于信任的行动将从起点到达目标的步数从362减少到190。十五个演示视频展示了LucidWM如何怀疑其梦境并依据该怀疑行动。视频可在以下网址获取：此https URL。
英文摘要：
World models enable agents to learn and plan in imagination, but predictions beyond their experience can become unreliable and mislead decisions. Existing uncertainty estimates derived from predictions can remain overconfident on unfamiliar state-action pairs. We propose the Lucid World Model (LucidWM), which learns doubt from experience and propagates trust through imagination. By integrating Subjective Logic into categorical latent transitions, LucidWM distinguishes predicted outcomes from their evidential support and assigns each transition a degree of doubt. The complement of this doubt defines transition-level trust, which accumulates multiplicatively along imagined trajectories to reweight returns for policy learning and guide action selection. Uncertainty estimation requires no additional parameters or forward passes. Evaluated on four base world models against seventeen uncertainty readouts, LucidWM detects environmental changes and signals uncertainty during action-corrupted rollouts. In a controlled navigation case study, acting on trust reduces the number of steps required to reach the goal from 362 to 190. Fifteen demonstration videos show how LucidWM doubts its dreams and acts on that doubt. Videos are available at https://lucidwm.github.io.
