ActBasis: Compact Action Parameterization for Fine-Grained Network Scheduling

Deterministic networks require coordinated routes and precise transmission times so that periodic messages meet deadlines without link conflicts. Higher timing precision increases the number of available start times within a fixed scheduling window, expanding the range of scheduling choices. Deep reinforcement learning can learn these coupled decisions from interaction, yet directly parameterizing an action-selection weight for every time-grid position on each link makes the actor output grow with scheduling precision. This raises the question of whether fine-grained action selection requires a correspondingly large actor output dimension. We propose ActBasis, which predicts compact coefficients over a fixed temporal basis to generate action-selection weights across the full scheduling grid. Together with routing weights, these action-selection weights guide the selection of a feasible route and transmission start time. Network experiments under shared feasibility constraints show that timing-score representations support effective action selection at high scheduling precision. ActBasis retains this performance level while reducing timing output dimension by up to a factor of 125 relative to direct matrix parameterization. Controlled experiments show that compact parameterization shapes scheduling behavior even before training. The results show that compact actor outputs can support fine-grained action selection while maintaining effective scheduling performance.