  Adjusting camera exposure in arbitrary lighting conditions is the first step
to ensure the functionality of computer vision applications. Poorly adjusted
camera exposure often leads to critical failure and performance degradation.
Traditional camera exposure control methods require multiple convergence steps
and time-consuming processes, making them unsuitable for dynamic lighting
conditions. In this paper, we propose a new camera exposure control framework
that rapidly controls camera exposure while performing real-time processing by
exploiting deep reinforcement learning. The proposed framework consists of four
contributions: 1) a simplified training ground to simulate real-world's diverse
and dynamic lighting changes, 2) flickering and image attribute-aware reward
design, along with lightweight state design for real-time processing, 3) a
static-to-dynamic lighting curriculum to gradually improve the agent's
exposure-adjusting capability, and 4) domain randomization techniques to
alleviate the limitation of the training ground and achieve seamless
generalization in the wild.As a result, our proposed method rapidly reaches a
desired exposure level within five steps with real-time processing (1 ms).
Also, the acquired images are well-exposed and show superiority in various
computer vision tasks, such as feature extraction and object detection.
