City metro network expansion, included in the transportation network design, aims to design new lines based on the existing metro network. Existing methods in the field of transportation network design either (i) can hardly formulate this problem efficiently, (ii) depend on expert guidance to produce solutions, or (iii) appeal to problem-specific heuristics which are difficult to design. To address these limitations, we propose a reinforcement learning based method for the city metro network expansion problem. In this method, we formulate the metro line expansion as a Markov decision process (MDP), which characterizes the problem as a process of sequential station selection. Then, we train an actor-critic model to design the next metro line on the basis of the existing metro network. The actor is an encoder-decoder network with an attention mechanism to generate the parameterized policy which is used to select the stations. The critic estimates the expected cumulative reward to assist the training of the actor by reducing training variance. The proposed method does not require expert guidance during design, since the learning procedure only relies on the reward calculation to tune the policy for better station selection. Also, it avoids the difficulty of heuristics designing by the policy formalizing the station selection. Considering origin-destination (OD) trips and social equity, we expand the current metro network in Xi’an, China, based on the real mobility information of 24,770,715 mobile phone users in the whole city. The results demonstrate the advantages of our method compared with existing approaches.