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.

翻译：我们提出了一种在因子图上实现期望自由能最小化的消息传递方法，该方法基于arXiv:2504.14898中提出的理论。通过将EFE最小化重新表述为带有认知先验的变分自由能最小化，我们将一个组合搜索问题转化为一个可通过标准变分技术求解的易处理推理问题。将我们的消息传递方法应用于因子化状态空间模型，能够实现高效策略推理。我们在具有认知不确定性的环境中评估了我们的方法：一个随机网格世界和一个部分可观察的Minigrid任务。在这些任务中，使用我们方法的智能体始终优于传统的KL控制智能体，表现出更稳健的规划和在不确定性下更高效的探索。在随机网格世界环境中，EFE最小化智能体避免了高风险路径，而在部分可观察的Minigrid设置中，它们进行了更系统化的信息寻求。该方法将主动推理理论与实际实现联系起来，为认知先验在人工智能体中的效率提供了实证证据。