Poster

Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization

Nathan Grinsztajn · Daniel Furelos-Blanco · Shikha Surana · ClĂ©ment Bonnet · Tom Barrett

Great Hall & Hall B1+B2 (level 1) #1500
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Thu 14 Dec 3 p.m. PST — 5 p.m. PST

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 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: traveling salesman, capacitated vehicle routing, and job-shop scheduling.

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