胜者为王：为组合优化训练高性能强化学习种群
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
AI总结：
本文提出Poppy，一种无需预定义多样性、通过无监督专门化训练互补策略种群的方法，在推理时同时展开以提升性能，并在四个NP困难问题上取得最先进RL结果。
摘要：
将强化学习（RL）应用于组合优化问题颇具吸引力，因为它无需专家知识或预先求解的实例。然而，由于这些（通常是NP-）困难问题固有的复杂性，期望智能体在推理时一次性解决它们是不现实的。因此，领先的方法通常采用额外的搜索策略，从随机采样和束搜索到显式微调。在本文中，我们主张学习一个互补策略种群的优势，该种群可以在推理时同时展开。为此，我们引入了Poppy，一种简单的种群训练程序。Poppy不依赖于预定义或手工设计的多样性概念，而是诱导一种无监督的专门化，其目标纯粹是最大化种群的整体性能。我们证明Poppy能产生一组互补策略，并在四个流行的NP困难问题：旅行商问题、容量车辆路径问题、0-1背包问题和作业车间调度问题上取得了最先进的RL结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
