论文标题
多代理自动化机器学习
Multi-Agent Automated Machine Learning
论文作者
论文摘要
在本文中，我们提出了多代理自动化机器学习（MA2ML），目的是有效处理自动化机器学习中模块的关节优化（AUTOML）。 MA2ML将每个机器学习模块（例如数据增强（AUG），神经体系结构搜索（NAS）或Hyper-Parameters（HPO））作为代理商和最终表现作为奖励，以制定多代理增强学习问题。 MA2ML根据其边际贡献向每个代理商明确分配信贷，以增强模块之间的合作，并结合范围的学习以提高搜索效率。从理论上讲，MA2ML保证关节优化的单调改进。广泛的实验表明，在计算成本的限制下，MA2ML在Imagenet上产生了最先进的TOP-1准确性，例如$ 79.7 \％/80.5 \％$，而Flops少于600m/800m。广泛的消融研究验证了MA2ML的信用分配和非政策学习的好处。
In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML). MA2ML takes each machine learning module, such as data augmentation (AUG), neural architecture search (NAS), or hyper-parameters (HPO), as an agent and the final performance as the reward, to formulate a multi-agent reinforcement learning problem. MA2ML explicitly assigns credit to each agent according to its marginal contribution to enhance cooperation among modules, and incorporates off-policy learning to improve search efficiency. Theoretically, MA2ML guarantees monotonic improvement of joint optimization. Extensive experiments show that MA2ML yields the state-of-the-art top-1 accuracy on ImageNet under constraints of computational cost, e.g., $79.7\%/80.5\%$ with FLOPs fewer than 600M/800M. Extensive ablation studies verify the benefits of credit assignment and off-policy learning of MA2ML.
