Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
UDC: A unified neural divide-and-conquer framework for large-scale combinatorial optimization problems
Single-stage neural combinatorial optimization solvers have achieved near-optimal results
on various small-scale combinatorial optimization (CO) problems without requiring expert …
on various small-scale combinatorial optimization (CO) problems without requiring expert …
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 …
practical relevance but present challenges due to their NP-hard combinatorial nature, hard …
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design
Handcrafting heuristics for solving complex planning tasks (eg, NP-hard combinatorial
optimization (CO) problems) is a common practice but requires extensive domain …
optimization (CO) problems) is a common practice but requires extensive domain …
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
The profiled vehicle routing problem (PVRP) is a generalization of the heterogeneous
capacitated vehicle routing problem (HCVRP) in which the objective is to optimize the routes …
capacitated vehicle routing problem (HCVRP) in which the objective is to optimize the routes …