  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.
