PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization
Parallel autoregressive models for multi-agent combinatorial optimization.
Read overviewResearch & Methods
Research explained. Methods examined.
Explore papers from Omelet’s research archive: optimization methods, learning and search, and multi-agent systems.
Research & Methods
14 items
LPMARL: Linear Programming-based Task Assignment for Hierarchical Multi-agent Reinforcement Learning
Linear-programming-based task assignment in hierarchical multi-agent reinforcement learning.
Read overviewRRNCO: Towards Real-World Routing with Neural Combinatorial Optimization
Neural combinatorial optimization for routing problems with real-world requirements.
Read overviewNeural Embedded Mixed-Integer Optimization for Location-Routing Problems
Neural models embedded in mixed-integer optimization for location-routing problems.
Read overviewRL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
A reinforcement-learning benchmark and library for combinatorial optimization.
Read overviewNeural Genetic Search in Discrete Spaces
Neural genetic search over discrete solution spaces.
Read overviewCAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
Collaborative attention and vehicle profiles for routing.
Read overviewAnt Colony Sampling with GFlowNets for Combinatorial Optimization
GFlowNets and ant colony sampling for combinatorial optimization.
Read overviewGenerative Flows on Synthetic Pathway for Drug Design
Generative flows over synthetic pathways for drug design.
Read overviewGenetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems
Neural cost prediction within genetic algorithms for hierarchical routing.
Read overviewRouteFinder: Towards Foundation Models for Vehicle Routing Problems
Foundation models for vehicle routing across problem settings.
Read overviewHiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
Heuristics-informed policies for large-scale multi-agent pathfinding.
Read overviewEquity-Transformer: Solving NP-hard Min-max Routing Problems as Sequential Generation with Equity Context
Sequential generation with equity context for min-max routing.
Read overviewA Neural Separation Algorithm for the Rounded Capacity Inequalities
Neural separation of rounded capacity inequalities for vehicle routing.
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