Papers › Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation

Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation

19 Oct 2019arXiv:1910.09959archive 2025-07-28

Yijiong Lin, Jiancong Huang, Matthieu Zimmer, Juan Rojas, Paul Weng

Deep reinforcement learning (DRL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. We propose two novel data augmentation techniques for DRL in order to reuse more efficiently observed data. The first one called Kaleidoscope Experience Replay exploits reflectional symmetries, while the second called Goal-augmented Experience Replay takes advantage of lax goal definitions. Our preliminary experimental results show a large increase in learning speed.

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Data Augmentation Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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Experience Replay

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