실제로 쓰이는 연구.
생성 모델과 탐색부터 경로 최적화, 스케줄링, 다중 에이전트 의사결정까지, 최적화 지능의 바탕이 되는 연구를 살펴봅니다.
논문 14편
LPMARL: Linear Programming-based Task Assignment for Hierarchical Multi-agent Reinforcement Learning
RRNCO: Towards Real-World Routing with Neural Combinatorial Optimization
Neural Embedded Mixed-Integer Optimization for Location-Routing Problems
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Neural Genetic Search in Discrete Spaces
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Generative Flows on Synthetic Pathway for Drug Design
Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems
RouteFinder: Towards Foundation Models for Vehicle Routing Problems
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
Equity-Transformer: Solving NP-hard Min-max Routing Problems as Sequential Generation with Equity Context
A Neural Separation Algorithm for the Rounded Capacity Inequalities
일치하는 논문이 없습니다. 다른 검색어나 연도로 다시 찾아 주세요.
논문 정보는 오믈렛의 연구 아카이브에서 가져왔습니다. 전체 서지 사항과 게재 정보는 각 논문에서 확인할 수 있습니다.