Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning

ArXi:2605.10546v1 Announce Type: new Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early benchmarks rather than grounded in principled design. In this work, we show that observation resolution is a critical yet overlooked variable for policy learning: higher-resolution inputs can substantially improve both performance and generalization, provided the network architecture can process them effectively.