Reinforcement learning (RL) has achieved ideal performance in recommendation systems (RS) by taking care of both immediate and future rewards from users. However, the existing RL-based recommendation methods assume that only a single type of interaction behavior (e.g., clicking) exists between user and item, whereas practical recommendation scenarios involve multiple types of user interaction behaviors (e.g., adding to cart, purchasing). In this paper, we propose a Multi-Task Reinforcement Learning model for multi-behavior Recommendation (MTRL4Rec), which gives different actions for users’ different behaviors with a single agent. Specifically, we first introduce a modular network in which modules can be reused or isolated from each other to model the commonalities and differences between users’ behaviors. Then a task routing network is used to generate routes in the modular network for each behavior task. Finally, we adopt a hierarchical reinforcement learning architecture to improve the efficiency of MTRL4Rec. Experiments on two public datasets validated the effectiveness of MTRL4Rec.