An Improved Reinforcement Learning Algorithm for Learning to Branch

Qu, Qingyu; Li, Xijun; Zhou, Yunfan; Zeng, Jia; Yuan, Mingxuan; Wang, Jie; Lü, Jinhu; Liu, Kexin; Kun, M.

doi:10.48550/arxiv.2201.06213

Cited by 4 publications

(3 citation statements)

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“…For example, Qu et al employed reinforcement learning to solve the process of mixedinteger linear programming, designing a reinforcement-learning-based branching strategy [34]. Tang et al used reinforcement learning to select appropriate cutting planes for the branch-and-cut process [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning

Hu,

Li,

Luo

2024

Applied Sciences

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This paper presents a solution to address the challenges of unexpected events in the operation of metro trains, which can lead to increased delays and safety risks. An improved Q-learning algorithm is proposed to reschedule train timetables via incorporating train detention and different section running times as actions. To enhance computational efficiency and convergence rate, a simulated annealing dynamic factor is introduced to improve action selection strategies. Additionally, importance sampling is employed to evaluate different policies effectively. A case study of Shenzhen Metro is conducted to demonstrate the effectiveness of the proposed method. The results show that the method achieves convergence, fast computation speed, and real-time adjustment capabilities. Compared to traditional methods such as no adjustment, manual adjustment, and FIFO (First-In-First-Out), the proposed method significantly reduces the average total train delay by 54% and leads to more uniform train headways. The proposed method utilizes a limited number of variables for practical state descriptions, making it well suited for real-world applications. It also exhibits good scalability and transferability to other metro systems.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning

Hu,

Li,

Luo

2024

Applied Sciences

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“…Recent developments in deep learning have further advanced these methods. Examples include learning to branch [124]- [129], learning a node selection strategy [130], and learning to search from scratch [131].…”

Section: A Augmenting Existing Solversmentioning

confidence: 99%

A Comprehensive Review on Leveraging Machine Learning for Multi-Agent Path Finding

Alkazzi,

Okumura

2024

IEEE Access

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This review paper provides an in-depth analysis of the latest advancements in applying Machine Learning (ML) to solve the Multi-Agent Path Finding (MAPF) problem. The MAPF problem is about finding collision-free paths for multiple agents to travel from their source to goal locations in a known environment. This method underpins a range of advanced, large-scale automated systems, notably in warehouse logistics. The existing research on conventional MAPF is extensive; however, recent developments in ML have notably augmented the capabilities of MAPF techniques. This research seeks to thoroughly investigate the emerging field focused on using ML to help solve the MAPF problem. It aims to highlight the transformative potential of ML in enhancing the efficiency and effectiveness of multi-agent systems in navigating and coordinating in complex environments. Our study comprehensively examines the entire MAPF process, encompassing environment representation, path planning, and solution execution.

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“…Some works apply ML models to directly solve MILPs [29], [30], [31]. Others attempt to incorporate ML models into heuristic components in modern solvers [10], [12], [32], [33]. Gasse et al [11] proposed to represent MILP instances as bipartite graphs, and use graph neural networks (GNNs) to capture features for branching decisions.…”

Section: Machine Learning For Milpmentioning

confidence: 99%

G2MILP: Learning to Generate Mixed-Integer Linear Programming Instances for MILP Solvers

Wang,

Geng,

et al. 2023

Preprint

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There have been significant efforts devoted to developing advanced mixed-integer linear programming (MILP) solvers, which are powerful tools for solving various real-world optimization problems. Despite the achievements, the limited availability of real-world instances often results in sub-optimal decisions and biased evaluations, which motivates a suite of MILP instance generation techniques. However, these approaches either rely on expert-designed formulations or struggle to capture the rich features of real-world instances. Moreover, the task of generating challenging MILP instances---which are valuable resources for evaluating solvers and motivating more efficient algorithms---remains underexplored. To tackle these problems, we propose G2MILP, which to the best of our knowledge is the first deep generative framework for MILP instances. Specifically, G2MILP represents MILP instances as bipartite graphs and employs a masked variational autoencoder to iteratively corrupt and replace parts of the original graphs to generate new ones. We then propose a hardness-oriented scheme, which iteratively augments the generator by learning from the hardest instances, to enhance G2MILP to construct challenging MILP instances. Experiments demonstrate that G2MILP can generate realistic MILP instances to effectively facilitate downstream tasks. Moreover, G2MILP can generate difficult instances initializing from given datasets, and the boost of hardness can be orders of magnitude.

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An Improved Reinforcement Learning Algorithm for Learning to Branch

Cited by 4 publications

References 0 publications

Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning

Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning

A Comprehensive Review on Leveraging Machine Learning for Multi-Agent Path Finding

G2MILP: Learning to Generate Mixed-Integer Linear Programming Instances for MILP Solvers

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