We investigate the application of active inference in developing energy-efficient control agents for manufacturing systems. Active inference, rooted in neuroscience, provides a unified probabilistic framework integrating perception, learning, and action, with inherent uncertainty quantification elements. Our study explores deep active inference, an emerging field that combines deep learning with the active inference decision-making framework. Leveraging a deep active inference agent, we focus on controlling parallel and identical machine workstations to enhance energy efficiency. We address challenges posed by the problem's stochastic nature and delayed policy response by introducing tailored enhancements to existing agent architectures. Specifically, we introduce multi-step transition and hybrid horizon methods to mitigate the need for complex planning. Our experimental results demonstrate the effectiveness of these enhancements and highlight the potential of the active inference-based approach.

翻译：我们研究了主动推理在制造系统节能控制智能体开发中的应用。源自神经科学的主动推理提供了集成感知、学习与行动的统一概率框架，并具备内在的不确定性量化机制。本研究探索了深度主动推理这一新兴领域——它将深度学习与主动推理决策框架相结合。通过利用深度主动推理智能体，我们聚焦于控制并行同构机床工作站以提升能效。针对该问题的随机特性和策略响应延迟带来的挑战，我们通过引入定制化改进方案对现有智能体架构进行优化。具体而言，我们提出了多步转移与混合视界方法以降低复杂规划需求。实验结果验证了这些改进措施的有效性，并凸显了基于主动推理方法的潜力。