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Found 5 papers, 5 papers with code
PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization
Multi-agent combinatorial optimization problems such as routing and scheduling have great practical relevance but present challenges due to their NP-hard combinatorial nature, hard constraints on the number of possible agents, and hard-to-optimize objective functions.
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
This paper introduces the Generative Flow Ant Colony Sampler (GFACS), a neural-guided probabilistic search algorithm for solving combinatorial optimization (CO).
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
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.
Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context
Notably, our method achieves significant reductions of runtime, approximately 335 times, and cost values of about 53\% compared to a competitive heuristic (LKH3) in the case of 100 vehicles with 1, 000 cities of mTSP.
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
First, SML transforms the context embedding for subsequent adaptation of SAGE based on scale information.
