Synergetic-informed deep reinforcement learning for sustainable management of transportation networks with large action spaces

Synergetic-informed deep reinforcement learning for sustainable management of transportation networks with large action spaces

Lai, Li (The Hong Kong Polytechnic University)
Dong, You (The Hong Kong Polytechnic University)
Andriotis, C. (TU Delft Architectural Technology)
Wang, Aijun (Wuhan University of Technology)
Lei, Xiaoming (The Hong Kong Polytechnic University)

2024

Effective transportation network management systems should consider safety and sustainability objectives. Existing research on large-scale transportation network management often employs the assumption that bridges can be considered individually under these objectives. However, this simplification misses accurate system-level representations, induced by multiple components, network topology, and global maintenance actions. To address these limitations, this paper presents a deep reinforcement learning (DRL) framework that draws inspiration from biological learning behaviors to determine optimal life-cycle management policies. It incorporates synergetic branches and hierarchical rewards, factorizing the action space and, thereby, diminishing system complexity from exponential to linear with respect to the number of bridges. Extensive experiments based on a realistic case study demonstrate that the proposed method outperforms expert maintenance strategies and state-of-the-art decision-making methods. Overall, the proposed DRL framework can assist engineers by offering adaptive solutions to maintenance planning. It also provides solutions that address large action spaces within complex systems.

deep reinforcement learning
infrastructure management
maintenance optimization
hierarchical reward
life-cycle analysis
large discrete action spaces

http://resolver.tudelft.nl/uuid:a4fb1d7a-7ad7-4a7f-a8ad-3a3dd7e20d44

https://doi.org/10.1016/j.autcon.2024.105302

2024-08-06

0926-5805

Automation in Construction, 160

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2024 Li Lai, You Dong, C. Andriotis, Aijun Wang, Xiaoming Lei