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Zicheng Su

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AAAI Conference 2025 Conference Paper

DFF: Decision-Focused Fine-Tuning for Smarter Predict-Then-Optimize with Limited Data

Jiaqi Yang
Enming Liang
Zicheng Su
Zhichao Zou
Peng Zhen
Jiecheng Guo
Wanjing Ma
Kun An

Decision-focused learning (DFL) offers an end-to-end approach to the predict-then-optimize (PO) framework by training predictive models directly on decision loss (DL), enhancing decision-making performance within PO contexts. However, the implementation of DFL poses distinct challenges. Primarily, DL can result in deviation from the physical significance of the predictions under limited data. Additionally, some predictive models are non-differentiable or black-box, which cannot be adjusted using gradient-based methods. To tackle the above challenges, we propose a novel framework, Decision-Focused Fine-tuning (DFF), which embeds the DFL module into the PO pipeline via a novel bias correction module. DFF is formulated as a constrained optimization problem that maintains the proximity of the DL-enhanced model to the original predictive model within a defined trust region. We theoretically prove that DFF strictly confines prediction bias within a predetermined upper bound, even with limited datasets, thereby substantially reducing prediction shifts caused by DL under limited data. Furthermore, the bias correction module can be integrated into diverse predictive models, enhancing adaptability to a broad range of PO tasks. Extensive evaluations on synthetic and real-world datasets, including network flow, portfolio optimization, and resource allocation problems with different predictive models, demonstrate that DFF not only improves decision performance but also adheres to fine-tuning constraints, showcasing robust adaptability across various scenarios.

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AAAI Conference 2022 Conference Paper

OAM: An Option-Action Reinforcement Learning Framework for Universal Multi-Intersection Control

Enming Liang
Zicheng Su
Chilin Fang
Renxin Zhong

Efficient traffic signal control is an important means to alleviate urban traffic congestion. Reinforcement learning (RL) has shown great potentials in devising optimal signal plans that can adapt to dynamic traffic congestion. However, several challenges still need to be overcome. Firstly, a paradigm of state, action, and reward design is needed, especially for an optimality-guaranteed reward function. Secondly, the generalization of the RL algorithms is hindered by the varied topologies and physical properties of intersections. Lastly, enhancing the cooperation between intersections is needed for large network applications. To address these issues, the Option-Action RL framework for universal Multiintersection control (OAM) is proposed. Based on the wellknown cell transmission model, we first define a lane-celllevel state to better model the traffic flow propagation. Based on this physical queuing dynamics, we propose a regularized delay as the reward to facilitate temporal credit assignment while maintaining the equivalence with minimizing the average travel time. We then recapitulate the phase actions as the constrained combinations of lane options and design a universal neural network structure to realize model generalization to any intersection with any phase definition. The multipleintersection cooperation is then rigorously discussed using the potential game theory. We test the OAM algorithm under four networks with different settings, including a city-level scenario with 2, 048 intersections using synthetic and real-world datasets. The results show that the OAM can outperform the state-of-the-art controllers in reducing the average travel time.

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