Multi-Asset Inspection and Maintenance Scheduling With Multi-Agent DRL

The patent replaces conventional mechanical/mathematical optimization methods (threshold-based formulations, decision tree analysis, renewal theory, stochastic optimal control) with a deep reinforcement learning system. The DRL agent learns optimal I&M policies through interaction with the infrastructure environment, substituting complex mathematical programming with neural network-based decision-making that adapts to noisy observations and dynamic conditions.

The DRL system performs self-learning and self-optimization by interacting with the infrastructure environment, observing deterioration patterns, and autonomously developing optimal inspection and maintenance policies. The agent continuously improves its decision-making capability through experience accumulation without requiring manual reprogramming or complex mathematical formulations.

The patent segments the infrastructure system into multiple assets or components, each managed by independent DRL agents or modular components of the DRL system. This segmentation allows the complex multi-asset optimization problem to be divided into manageable sub-problems that can be solved through distributed learning and decision-making, reducing overall computational burden while maintaining solution quality.

The patent transitions from traditional multi-dimensional optimization problems (with multiple constraints and objectives) to a reinforcement learning framework where the agent learns policies through high-dimensional state-action spaces. The neural network handles complexity through its parameter space rather than through mathematical optimization, effectively managing large networks through learned representations rather than exhaustive search.

The patent implements dynamic adaptation through the reinforcement learning agent, which continuously learns from observations of infrastructure deterioration and changing conditions. The policy network adapts its decisions based on real-time state information, allowing the system to respond to dynamic objectives and evolving infrastructure conditions without requiring re-formulation of the optimization problem.

The DRL system incorporates feedback loops where the agent observes the results of maintenance actions, updates its belief about infrastructure state, and adjusts future decisions accordingly. This feedback mechanism enables the system to adapt to dynamic conditions and learn from past experiences, transforming static mathematical formulations into dynamic, learning-based decision-making.

The patent applies beforehand cushioning by incorporating noise robustness into the reinforcement learning framework from the outset. The agent is trained to handle noisy and ambiguous observations through techniques like partial observability Markov decision processes (POMDPs) and probabilistic transition models, preparing the system in advance to deal with real-world data quality issues without compromising decision reliability.

The patent introduces intermediary layers in the data processing pipeline, including sensor fusion modules, data cleaning components, and probabilistic models that mediate between raw noisy observations and decision-making. These intermediaries filter, validate, and interpret noisy data from multiple sources before feeding it to the DRL agent, reducing the direct impact of data noise on decision reliability.