标题： rQdia：使用图像增强正则化Q值分布 显示英文标题

标题： rQdia: Regularizing Q-Value Distributions With Image Augmentation

摘要： rQdia通过在基于像素的深度强化学习中使用增强图像来对Q值分布进行正则化。通过一个简单的辅助损失，该损失通过均方误差使这些分布相等，rQdia在MuJoCo连续控制套件的9/12和10/12任务中分别提升了DrQ和SAC，并在Data-Efficient Rainbow的18/26个Atari街机环境中取得了提升。收益在样本效率和长期训练中都有体现。此外，添加rQdia最终使无模型的连续控制从像素中超越了状态编码基线。 显示英文摘要

摘要： 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.