1 code implementation • 28 Oct 2024 • Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang
Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints.
no code implementations • 7 Aug 2024 • Yong Liang Goh, Zhiguang Cao, Yining Ma, Yanfei Dong, Mohammed Haroon Dupty, Wee Sun Lee
Existing neural constructive solvers for routing problems have predominantly employed transformer architectures, conceptualizing the route construction as a set-to-sequence learning task.
2 code implementations • 2 May 2024 • Jianan Zhou, Zhiguang Cao, Yaoxin Wu, Wen Song, Yining Ma, Jie Zhang, Chi Xu
Learning to solve vehicle routing problems (VRPs) has garnered much attention.
no code implementations • 12 Apr 2024 • Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yining Ma, Yue-Jiao Gong
Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum.
no code implementations • 4 Mar 2024 • Hongshu Guo, Yining Ma, Zeyuan Ma, Jiacheng Chen, Xinglin Zhang, Zhiguang Cao, Jun Zhang, Yue-Jiao Gong
As a proof-of-principle study, we apply this framework to a group of Differential Evolution algorithms.
no code implementations • 2 Mar 2024 • Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Guojun Peng, Zhiguang Cao, Yining Ma, Yue-Jiao Gong
Recent research explores optimization using large language models (LLMs) by either iteratively seeking next-step solutions from LLMs or directly prompting LLMs for an optimizer.
no code implementations • 14 Feb 2024 • Yingpeng Du, Ziyan Wang, Zhu Sun, Haoyan Chua, Hongzhi Liu, Zhonghai Wu, Yining Ma, Jie Zhang, Youchen Sun
To adapt text-based LLMs with structured graphs, We use the LLM as an aggregator in graph processing, allowing it to understand graph-based information step by step.
1 code implementation • 4 Feb 2024 • Jiacheng Chen, Zeyuan Ma, Hongshu Guo, Yining Ma, Jie Zhang, Yue-Jiao Gong
Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers.
1 code implementation • NeurIPS 2023 • Yining Ma, Zhiguang Cao, Yeow Meng Chee
In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for routing problems.
1 code implementation • NeurIPS 2023 • Jinbiao Chen, Zizhen Zhang, Zhiguang Cao, Yaoxin Wu, Yining Ma, Te Ye, Jiahai Wang
Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set.
1 code implementation • NeurIPS 2023 • Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Zhenrui Li, Guojun Peng, Yue-Jiao Gong, Yining Ma, Zhiguang Cao
To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods.
3 code implementations • 29 Jun 2023 • Federico Berto, Chuanbo Hua, Junyoung Park, Laurin Luttmann, Yining Ma, Fanchen Bu, Jiarui Wang, Haoran Ye, Minsu Kim, Sanghyeok Choi, Nayeli Gast Zepeda, André Hottung, Jianan Zhou, Jieyi Bi, Yu Hu, Fei Liu, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Davide Angioni, Wouter Kool, Zhiguang Cao, Qingfu Zhang, Joungho Kim, Jie Zhang, Kijung Shin, Cathy Wu, Sungsoo Ahn, Guojie Song, Changhyun Kwon, Kevin Tierney, Lin Xie, Jinkyoo Park
To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.
no code implementations • 12 Jun 2023 • Yining Ma, Wei Jiang, Lingtong Zhang, Junyi Chen, Hong Wang, Chen Lv, Xuesong Wang, Lu Xiong
Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs.
no code implementations • 26 Jan 2023 • Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low, Roger Wattenhofer
Federated Reinforcement Learning (FedRL) encourages distributed agents to learn collectively from each other's experience to improve their performance without exchanging their raw trajectories.
no code implementations • 22 Nov 2022 • Jing Sun, Shuo Chen, Cong Zhang, Yining Ma, Jie Zhang
To address this issue, we introduce Distributional Opponent-aided Multi-agent Actor-Critic (DOMAC), the first speculative opponent modelling algorithm that relies solely on local information (i. e., the controlled agent's observations, actions, and rewards).
1 code implementation • 14 Oct 2022 • Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee
Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i. e., uniform).
2 code implementations • 25 Apr 2022 • Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Hongliang Guo, YueJiao Gong, Yeow Meng Chee
We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs).
2 code implementations • NeurIPS 2021 • Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low
The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-making policy without sharing raw trajectories.
2 code implementations • NeurIPS 2021 • Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang
Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).
1 code implementation • 6 Oct 2021 • Jingwen Li, Yining Ma, Ruize Gao, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step.