用运动先验增强强化学习，让目标定位更准更稳。
Motion-Aware Reinforcement Learning For Object Localization
- 引入恒定速度运动先验和动作平滑惩罚，优化定位策略。
- 在Pascal VOC和VisDrone上分别提升0.011和0.007的检测成功率。
- 适合需要稳定高精度定位的视觉追踪与检测场景。
我们提出MARLNet（运动感知强化学习网络），一种基于PPO的边界框精修代理，将恒定速度运动先验融入观测状态，并在奖励函数中加入动作平滑惩罚。该代理处理268维观测，包含当前候选框、运动预测、前一动作及256维EfficientNet-B0裁剪特征，学习控制坐标调整的五维策略和二值终止触发器。在Pascal VOC 2012和VisDrone 2019上评估，MARLNet在所有测试正则化强度下训练稳定，在IoU≥0.5时检测成功率达到提升：在VOC上最高+0.011（λ_phys=0.10），运动先验防止了普通PPO的过调问题；在VisDrone上提升+0.007（λ_phys=0.70），因基础检测器较弱，未约束的PPO反而取得更大增益（+0.025）。通过奖励设计消融与训练动态分析，发现将恒定速度偏差惩罚与绝对IoU项结合会导致触发器崩溃，替换为动作平滑惩罚可解决此问题。进一步通过全局加局部观测消融证实，共享骨干网络的裁剪特征精修代理存在表征上限。
原文摘要 · Abstract (English)
We present MARLNet (Motion-Aware Reinforcement Learning Network), a PPO-based bounding-box refinement agent that incorporates a constant-velocity motion prior into the observation state and an action smoothness penalty into the reward function. The agent operates on 268-dimensional observations encoding the current proposal, a kinematic prediction, the previous action, and a 256-dimensional EfficientNet-B0 crop feature, and learns a five-dimensional policy controlling coordinate adjustments and a binary termination trigger. Evaluated on Pascal VOC 2012 and VisDrone 2019, MARLNet trains stably across all regularization strengths tested and achieves consistent gains in detection success rate at $\text{IoU} \geq 0.5$: up to $+0.011$ on VOC ($λ_\text{phys}{=}0.10$), where the motion prior prevents the overshooting that causes plain PPO to regress on this metric, and $+0.007$ on VisDrone ($λ_\text{phys}{=}0.70$), where unconstrained PPO achieves a larger gain ($+0.025$) owing to the weaker base detector. Through reward design ablations and training dynamics analysis, we identify a reward interference in which combining a constant-velocity deviation penalty with an absolute IoU term causes trigger collapse, and show that replacing it with the action smoothness penalty resolves this failure. We further characterize a representational ceiling facing crop-feature refinement agents that share a backbone with their base detector, confirmed through a global-plus-local observation ablation. Project page: https://prithviraj97.github.io/marl-net
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