Activities are the fundamental components of Android applications (apps). However, existing approaches to automated testing for Android apps cannot effectively manage the transitions between activities, e.g., too rarely or too often. Besides, some techniques need to repeatedly restart from scratch and revisit every intermediate activity to reach a specific one, which leads to unnecessarily long transitions and wasted time. To address these problems, we propose AimDroid, a practical model-based approach to automated testing for Android apps that aims to manage the exploration of activities and meantime minimize unnecessary transitions between them. Specifically, AimDroid applies an activity-insulated multi-level strategy during testing and replaying. It systematically discovers unexplored activities and then intensively exploits every discovered individual with a reinforcement learning guided random algorithm. We conduct comprehensive experiments on 50 popular closed-source commercial apps that in total have billions of daily usages in China. The results demonstrate that AimDroid outperforms both Sapienz and Monkey in activity, method and instruction coverage, respectively. In addition, AimDroid also reports more crashes than the other two.