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Population-Based Reinforcement Learning for Combinatorial Optimization

2022-10-07 11:58:08
Nathan Grinsztajn, Daniel Furelos-Blanco, Thomas D. Barrett

Abstract

Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic to expect an agent to solve these (often NP-)hard problems in a single shot at inference due to their inherent complexity. Thus, leading approaches often implement additional search strategies, from stochastic sampling and beam-search to explicit fine-tuning. In this paper, we argue for the benefits of learning a population of complementary policies, which can be simultaneously rolled out at inference. To this end, we introduce Poppy, a simple theoretically grounded training procedure for populations. Instead of relying on a predefined or hand-crafted notion of diversity, Poppy induces an unsupervised specialization targeted solely at maximizing the performance of the population. We show that Poppy produces a set of complementary policies, and obtains state-of-the-art RL results on three popular NP-hard problems: the traveling salesman (TSP), the capacitated vehicle routing (CVRP), and 0-1 knapsack (KP) problems. On TSP specifically, Poppy outperforms the previous state-of-the-art, dividing the optimality gap by 5 while reducing the inference time by more than an order of magnitude.

Abstract (translated)

URL

https://arxiv.org/abs/2210.03475

PDF

https://arxiv.org/pdf/2210.03475.pdf


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