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

• URL: http://arxiv.org/abs/2210.03475v2
• Date: Mon, 13 Nov 2023 23:32:29 GMT
• Title: Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
• Authors: Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana, Cl\'ement Bonnet, Thomas D. Barrett
• Abstract summary: We argue for the benefits of learning a population of complementary policies, which can be simultaneously rolled out at inference. We show that Poppy produces a set of complementary policies, and obtains state-of-the-art RL results on four popular NP-hard problems.
• Score: 6.6765384699410095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 four popular NP-hard problems: traveling salesman, capacitated vehicle routing, 0-1 knapsack, and job-shop scheduling.

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