![[2401.12455] Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2401.12455] Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management
Summary
This article presents a novel framework for managing transportation infrastructure using multi-agent deep reinforcement learning, addressing key challenges in scalability and optimality.
Why It Matters
The research tackles significant limitations in traditional transportation management methods by introducing a centralized training and decentralized execution approach. This is crucial for improving decision-making in complex, high-dimensional environments, ultimately leading to better infrastructure management and resource allocation.
Key Takeaways
- Introduces DDMAC-CTDE, a new reinforcement learning architecture for transportation management.
- Addresses constraints in decision-making processes using constrained POMDPs.
- Demonstrates improved performance over traditional management baselines in a benchmark environment.
Computer Science > Multiagent Systems arXiv:2401.12455 (cs) [Submitted on 23 Jan 2024 (v1), last revised 25 Feb 2026 (this version, v2)] Title:Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management Authors:M. Saifullah, K.G. Papakonstantinou, A. Bhattacharya, S.M. Stoffels, C.P. Andriotis View a PDF of the paper titled Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management, by M. Saifullah and 4 other authors View PDF HTML (experimental) Abstract:Life-cycle management of large-scale transportation systems requires determining a sequence of inspection and maintenance decisions to minimize long-term risks and costs while dealing with multiple uncertainties and constraints that lie in high-dimensional spaces. Traditional approaches have been widely applied but often suffer from limitations related to optimality, scalability, and the ability to properly handle uncertainty. Moreover, many existing methods rely on unconstrained formulations that overlook critical operational constraints. We address these issues in this work by casting the optimization problem within the framework of constrained Partially Observable Markov Decision Processes (POMDPs), which provide a robust mathematical foundation for stochastic sequential decision-making under observation uncertainties, in the presence of risk and resource limitat...
