Microservice, an architectural design that decomposes applications into loosely coupled services, is adopted in modern software design, including cloud-based scientific workflow processing. The microservice design makes scientific workflow systems more modular, more flexible, and easier to develop. However, cloud deployment of microservice workflow execution systems doesn't come for free, and proper resource management decisions have to be made in order to achieve certain performance objective (e.g., response time) within constraint operation cost. Nevertheless, effective online resource allocation decisions are hard to achieve due to dynamic workloads and the complicated interactions of microservices in each workflow. In this paper, we propose an adaptive resource allocation approach for microservice workflow system based on recent advances in reinforcement learning. Our approach (1) assumes little prior knowledge of the microservice workflow system and does not require any elaborately designed model or crafted representative simulator of the underlying system, and (2) avoids high sample complexity which is a common drawback of model-free reinforcement learning when applied to real-world scenarios. We show that our proposed approach automatically achieves effective policy for resource allocation with limited number of time-consuming interactions with the microservice workflow system. We perform extensive evaluations to validate the effectiveness of our approach and demonstrate that it outperforms existing resource allocation approaches with read-world emulated workflows.