# Learning to Control Camera Exposure via Reinforcement Learning

arXiv: https://arxiv.org/abs/2404.01636 · HTML: https://mlsanity.com/paper/2404.01636/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Placed in

- Deep reinforcement learning methods / [RL with diffusion and flow models](https://mlsanity.com/deeprl/rl-with-diffusion-and-flow-models/)

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Monocular Depth Estimation Based On Deep Learning: An Overview](https://mlsanity.com/paper/2003.06620/) (2020)
- [Depth Completion using Plane-Residual Representation](https://mlsanity.com/paper/2104.07350/) (2021)
- [Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching](https://mlsanity.com/paper/2102.04341/) (2021)
- [Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment](https://mlsanity.com/paper/1907.12646/) (2019)
- [Gradient-based Camera Exposure Control for Outdoor Mobile Platforms](https://mlsanity.com/paper/1708.07338/) (2017)
- [DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning](https://mlsanity.com/paper/2207.03081/) (2022)

## Built on by (most cited first, 9 of 9)

- [Efficient Camera Exposure Control for Visual Odometry via Deep Reinforcement Learning](https://mlsanity.com/paper/2408.17005/) (2024)
- [RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes](https://mlsanity.com/paper/2601.05249/) (2026)
- [BiasBench: A reproducible benchmark for tuning the biases of event cameras](https://mlsanity.com/paper/2504.18235/) (2025)
- [CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing](https://mlsanity.com/paper/2505.23102/) (2025)
- [Active Illumination Control in Low-Light Environments using NightHawk](https://mlsanity.com/paper/2506.06394/) (2025)
- [Adaptive Illumination Control for Robot Perception](https://mlsanity.com/paper/2602.15900/) (2026)
- [BOCCHI: A More Realistic and Challenging Benchmark for Local Motion Blur Detection with MSDCT-UNet](https://mlsanity.com/paper/2607.10427/) (2026)
- [GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking](https://mlsanity.com/paper/2607.15699/) (2026)
- [Neural Block-wise Auto-exposure for Single-Shot High Dynamic Range Imaging](https://mlsanity.com/paper/2610.18733/) (2026)

## Abstract (arXiv)

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.