Deep reinforcement learning-driven multi-objective optimization and its applications on lighting infrastructure operation and maintenance strategy

This study addresses the challenges facing tunnel lighting system maintenance, where conventional single-objective optimization strategies and traditional maintenance approaches struggle to balance multidimensional requirements. Focusing on lifecycle maintenance management of tunnel lighting infrastructure, the research transforms multi-objective optimization into a set of Pareto-optimal subproblems through decomposition strategies. The proposed framework establishes a dynamic topological network within the solution space by integrating the Double Deep Q-Network(DDQN) algorithm from deep reinforcement learning with neighborhood gradient transfer strategies. This study proposes an innovative integration of Wiener degradation processes and the DDQN algorithm to establish a dynamic reliability-cost coupling model for equipment performance analysis. A multi-objective deep reinforcement learning (MODRL)-driven intelligent maintenance framework is developed, systematically coordinating degradation dynamics and economic constraints through computational learning mechanisms. The results show that incorporating maintenance costs and reliability as reward components in the multi-objective optimization problem (MOP) simultaneously enhances operational reliability and reduces comprehensive maintenance expenditures by 29.7%. The neighborhood-based parameter transfer strategy reduced single-episode training time by 41.9% and parameter synchronization time by 68.3%, while improving GPU utilization by 34.9%. It achieved faster convergence with 22.8 fewer threshold steps and reduced multi-objective conflict rates by 17.0%. The developed multi-objective optimization framework for tunnel lighting systems overcomes fixed maintenance threshold limitations. The framework demonstrated a 68.5% reliability decline near lighting failure conditions while effectively addressing overconfidence issues. The weight-combination-based adaptive mechanism enables scenario-specific customization of optimization objectives, offering scalable solutions for cost-prioritized, reliability-focused, or balanced operational strategies.