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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.21367 |
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| _version_ | 1866913914172735488 |
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| author | Lerman, Sam Bi, Jing |
| author_facet | Lerman, Sam Bi, Jing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | rQdia: Regularizing Q-Value Distributions With Image Augmentation Lerman, Sam Bi, Jing Machine Learning Artificial Intelligence 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. |
| title | rQdia: Regularizing Q-Value Distributions With Image Augmentation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.21367 |