Neural Architecture Search (NAS) achieved many breakthroughs in recent years. In spite of its remarkable progress, many algorithms are restricted to particular search spaces. They also lack efficient mechanisms to reuse knowledge when confronting multiple tasks. These challenges preclude their applicability, and motivate our proposal of CATCH, a novel Context-bAsed meTa reinforcement learning (RL) algorithm for transferrable arChitecture searcH. The combination of meta-learning and RL allows CATCH to efficiently adapt to new tasks while being agnostic to search spaces. CATCH utilizes a probabilistic encoder to encode task properties into latent context variables, which then guide CATCH\textquoteright s controller to quickly "catch" top-performing networks. The contexts also assist a network evaluator in filtering inferior candidates and speed up learning. Extensive experiments demonstrate CATCH\textquoteright s universality and search efficiency over many other widely-recognized algorithms. It is also capable of handling cross-domain architecture search as competitive networks on ImageNet, COCO, and Cityscapes are identified. This is the first work to our knowledge that proposes an efficient transferrable NAS solution while maintaining robustness across various settings.

提出了一种名为CATCH的基于上下文的元强化学习算法，它通过利用元学习和强化学习结合的方法，在转移学习的场景下高效地搜索出最优网络结构，并在ImageNet、COCO和Cityscapes数据集上展示出了鲁棒性。该算法通过将任务属性编码成潜在上下文变量，然后指导其控制器快速捕捉表现最佳的网络。

基于环境感知元强化学习的可迁移网络结构搜索