Search Results for author: Yining Ma

Found 20 papers, 12 papers with code

Learning to Handle Complex Constraints for Vehicle Routing Problems

1 code implementation28 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.

Decoder Traveling Salesman Problem

Hierarchical Neural Constructive Solver for Real-world TSP Scenarios

no code implementations7 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.

Traveling Salesman Problem

Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning

no code implementations12 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.

LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation

no code implementations2 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.

Code Generation Contrastive Learning

Large Language Model with Graph Convolution for Recommendation

no code implementations14 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.

Hallucination Language Modelling +1

Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning

1 code implementation4 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.

Meta-Learning Zero-shot Generalization

Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

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.

Combinatorial Optimization Diversity +1

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

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.

Benchmarking

Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles

no code implementations12 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.

FedHQL: Federated Heterogeneous Q-Learning

no code implementations26 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.

Q-Learning reinforcement-learning +2

Decision-making with Speculative Opponent Models

no code implementations22 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).

Decision Making SMAC+ +1

Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation

1 code implementation14 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).

Knowledge Distillation

Efficient Neural Neighborhood Search for Pickup and Delivery Problems

2 code implementations25 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).

Diversity

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

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.

Decision Making Federated Learning +3

Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer

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).

Traveling Salesman Problem

Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem

1 code implementation6 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.

Decoder reinforcement-learning +1

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