Title: Graph Contrastive Learning with Reinforced Augmentation. Abstract: Graph contrastive learning (GCL), designing contrastive objectives to learn embeddings from augmented graphs, has become a prevailing method for learning embeddings from graphs in an unsupervised manner. As an important procedure in GCL, graph data augmentation (GDA) directly affects the model performance on the downstream task. Currently, there are three types of GDA strategies: trial-and-error, precomputed method, and adversarial method. However, these strategies ignore the connection between the two consecutive augmentation results because GDA is regarded as an independent process. In this paper, we regard the GDA in GCL as a Markov decision process. Based on this point, we propose a reinforced method, i.e., the fourth type of GDA strategy, using a novel Graph Advantage Actor-Critic (GA2C) model for GCL. On 23 graph datasets, the experimental results verify that GA2C outperforms the SOTA GCL models on a series of downstream tasks such as graph classification, node classification, and link prediction.