用消息传递法实现预期自由能最小化，提升智能体在不确定环境中的规划与探索能力。
A Message Passing Realization of Expected Free Energy Minimization
- 将预期自由能最小化转化为带认知先验的变分自由能优化，转为可解的推断问题。
- 在随机网格世界和部分可观测迷你网格任务中，性能优于传统KL控制方法，更稳健且高效探索。
- 适合研究主动推理、强化学习中不确定性建模的学者，尤其关注信息获取与鲁棒决策。
我们提出一种基于因子图的预期自由能（EFE）最小化消息传递方法，其理论基础源自 arXiv:2504.14898。通过将EFE最小化重述为带有认知先验的变分自由能最小化，将原本复杂的组合搜索问题转化为可通过标准变分技术求解的可处理推断问题。将该消息传递方法应用于因子化状态空间模型，实现了高效的策略推断。我们在存在认知不确定性环境中评估该方法：一个随机网格世界和一个部分可观测的Minigrid任务。使用该方法的智能体在这些任务中持续优于传统KL控制智能体，展现出更强的鲁棒性规划能力和高效的探索行为。在随机网格世界中，EFE最小化智能体规避高风险路径；在部分可观测的Minigrid设置中，它们表现出更系统的信息寻求行为。该方法连接了主动推理理论与实际实现，为认知先验在人工智能体中的效率提供了实证支持。
原文摘要 · Abstract (English)
We present a message passing approach to Expected Free Energy (EFE) minimization on factor graphs, based on the theory introduced in arXiv:2504.14898. By reformulating EFE minimization as Variational Free Energy minimization with epistemic priors, we transform a combinatorial search problem into a tractable inference problem solvable through standard variational techniques. Applying our message passing method to factorized state-space models enables efficient policy inference. We evaluate our method on environments with epistemic uncertainty: a stochastic gridworld and a partially observable Minigrid task. Agents using our approach consistently outperform conventional KL-control agents on these tasks, showing more robust planning and efficient exploration under uncertainty. In the stochastic gridworld environment, EFE-minimizing agents avoid risky paths, while in the partially observable minigrid setting, they conduct more systematic information-seeking. This approach bridges active inference theory with practical implementations, providing empirical evidence for the efficiency of epistemic priors in artificial agents.
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