rQdia regularizes Q-value distributions with augmented images in pixel-based deep reinforcement learning. With a simple auxiliary loss, that equalizes these distributions via MSE, rQdia boosts DrQ and SAC on 9/12 and 10/12 tasks respectively in the MuJoCo Continuous Control Suite from pixels, and Data-Efficient Rainbow on 18/26 Atari Arcade environments. Gains are measured in both sample efficiency and longer-term training. Moreover, the addition of rQdia finally propels model-free continuous control from pixels over the state encoding baseline.
Cite
@article{arxiv.2506.21367,
title = {rQdia: Regularizing Q-Value Distributions With Image Augmentation},
author = {Sam Lerman and Jing Bi},
journal= {arXiv preprint arXiv:2506.21367},
year = {2025}
}