POMO: Policy Optimization with Multiple Optima for Reinforcement Learning

TL;DR

POMO is a reinforcement learning method that efficiently finds near-optimal solutions for various NP-hard combinatorial optimization problems by exploiting symmetries and encouraging diverse solutions.

Contribution

The paper introduces POMO, a novel RL-based heuristic that improves training stability, solution diversity, and performance across multiple combinatorial optimization problems.

Findings

Achieves 0.14% optimality gap on TSP100

Outperforms recent learned heuristics on TSP, CVRP, and KP

Reduces inference time significantly

Abstract

In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a great potential in practical applications because it allows near-optimal solutions to be found without expert guides armed with substantial domain knowledge. We introduce Policy Optimization with Multiple Optima (POMO), an end-to-end approach for building such a heuristic solver. POMO is applicable to a wide range of CO problems. It is designed to exploit the symmetries in the representation of a CO solution. POMO uses a modified REINFORCE algorithm that forces diverse rollouts towards all optimal solutions. Empirically, the low-variance baseline of POMO makes RL training fast and stable, and it is more resistant to local minima compared to previous approaches. We also introduce a new augmentation-based inference method, which accompanies POMO nicely. We demonstrate the effectiveness of POMO by solving three popular NP-hard problems, namely, traveling salesman (TSP), capacitated vehicle routing (CVRP), and 0-1 knapsack (KP). For all three, our solver based on POMO shows a significant improvement in performance over all recent learned heuristics. In particular, we achieve the optimality gap of 0.14% with TSP100 while reducing inference time by more than an order of magnitude.