Mainstream high dynamic range (HDR) imaging techniques typically rely on fusing multiple images captured with different exposure setups (shutter speed and ISO). A good balance between shutter speed and ISO are critical for high-quality HDR, as high ISO introduce significant noise, whereas long shutter speeds may lead to noticeable motion blur—both. However, existing methods often overlook the complex interaction between shutter speed and ISO and fail to account for motion blur effects in dynamic scenes.In this work, we propose AdaptiveAE, a reinforcement learning-based method that optimizes the selection of shutter speed and ISO combinations to maximize HDR reconstruction quality in dynamic environments. AdaptiveAE integrates an image synthesis pipeline that incorporates motion blur and noise simulation in our training procedure and leveraging semantic information and exposure histogram. It can adaptively select optimal ISO and shutter speed sequences based on a user-defined exposure time budget, find a better exposure schedule than traditional fixed exposure solution. Experimental results across multiple datasets demonstrate that AdaptiveAE achieves state-of-the-art performance.