Reinforcement Learning in Vision: A Survey
Authors: name: Weijia Wu, name: Chen Gao, name: Joya Chen, name: Kevin Qinghong Lin, name: Qingwei Meng, name: Yiming Zhang, name: Yuke Qiu, name: Hong Zhou, name: Mike Zheng Shou

Recent advances at the intersection of reinforcement learning (RL) and visual
intelligence have enabled agents that not only perceive complex visual scenes
but also reason, generate, and act within them. This survey offers a critical
and up-to-date synthesis of the field. We first formalize visual RL problems
and trace the evolution of policy-optimization strategies from RLHF to
verifiable reward paradigms, and from Proximal Policy Optimization to Group
Relative Policy Optimization. We then organize more than 200 representative
works into four thematic pillars: multi-modal large language models, visual
generation, unified model frameworks, and vision-language-action models. For
each pillar we examine algorithmic design, reward engineering, benchmark
progress, and we distill trends such as curriculum-driven training,
preference-aligned diffusion, and unified reward modeling. Finally, we review
evaluation protocols spanning set-level fidelity, sample-level preference, and
state-level stability, and we identify open challenges that include sample
efficiency, generalization, and safe deployment. Our goal is to provide
researchers and practitioners with a coherent map of the rapidly expanding
landscape of visual RL and to highlight promising directions for future
inquiry. Resources are available at:
https://github.com/weijiawu/Awesome-Visual-Reinforcement-Learning.