This paper presents a novel method to plan interactive task based on Q-learning for intelligent characters. Firstly, in the data preprocessing phase, we abstract the motion clips as high-level behaviors and construct the interactive behavior graph to define the interactive capabilities in terms of interactive features. Secondly, for the controller training phase, Q-learning algorithm is employed to train the controller. Finally, in the motion-synthesis phase, the optimal motion sequences can be generated by following the controller to accomplish the interactive task. The experiments demonstrate that our framework can generate realistic motion sequences to plan interactive task in complex environment.