Interpretable Deep Reinforcement Learning for Element-level Bridge Life-cycle Optimization
Summary: arXiv:2604.02528v1 Announce Type: new
Abstract: The new Specifications for the National Bridge Inventory (SNBI), in effect from 2022, emphasize the use of element-level condition states (CS) for risk-based bridge management. Instead of a general component rating, element-level condition data use an array of relative CS quantities (i.e., CS proportions) to represent the condition of a bridge. Although this greatly increases the granularity of bridge condition data, it introduces challenges to set up optimal life-cycle policies due to the expanded state space from one single categorical integer to four-dimensional probability arrays.
This study proposes a new interpretable reinforcement learning (RL) approach to seek optimal life-cycle policies based on element-level state representations. Compared to existing RL methods, the proposed algorithm yields life-cycle policies in the form of oblique decision trees with reasonable amounts of nodes and depth, making them directly understandable and auditable by humans and easily implementable into current bridge management systems.
Key Features of the Proposed Approach
- Improved Actor Function Approximators: The use of differentiable soft tree models allows for more accurate approximations of policies.
- Temperature Annealing Process: This technique enhances training by adjusting the exploration-exploitation balance dynamically.
- Regularization and Pruning Rules: Implementing these strategies helps to limit policy complexity, ensuring the resulting models remain interpretable.
Collectively, these improvements can yield interpretable life-cycle policies in the form of deterministic oblique decision trees. The benefits and trade-offs from these techniques are demonstrated in both supervised and reinforcement learning settings. The resulting framework is illustrated in a life-cycle optimization problem for steel girder bridges.
Benefits of Interpretable Reinforcement Learning
- Enhanced Understanding: By producing policies in the form of decision trees, stakeholders can easily comprehend and trust the decision-making process.
- Auditability: The structure of the decision trees allows for straightforward audits, which is crucial for regulatory compliance in bridge management.
- Ease of Implementation: The compatibility of the proposed method with existing systems facilitates its adoption in real-world applications.
In conclusion, the proposed interpretable deep reinforcement learning approach offers a promising solution to the challenges posed by the new SNBI requirements. By focusing on element-level condition states and leveraging advanced machine learning techniques, this study paves the way for more effective and understandable bridge management strategies. The work not only addresses the technical complexities introduced by the new specifications but also ensures that the resulting life-cycle policies are both practical and transparent.