This paper introduces BarlowRL, a data-efficient reinforcement learning agent that combines the Barlow Twins self-supervised learning framework with DER (Data-Efficient Rainbow) algorithm. BarlowRL outperforms both DER and its contrastive counterpart CURL on the Atari 100k benchmark. BarlowRL avoids dimensional collapse by enforcing information spread to the whole space. This helps RL algorithms to utilize uniformly spread state representation that eventually results in a remarkable performance. The integration of Barlow Twins with DER enhances data efficiency and achieves superior performance in the RL tasks. BarlowRL demonstrates the potential of incorporating self-supervised learning techniques to improve RL algorithms.

翻译：本文提出BarlowRL，一种结合巴洛双生自监督学习框架与DER（数据高效彩虹）算法的数据高效强化学习智能体。在Atari 100k基准测试中，BarlowRL在性能上超越了DER及其对比学习对应方法CURL。BarlowRL通过强制信息分布到整个空间，避免了维度坍缩问题。这有助于强化学习算法利用均匀分布的状态表征，最终实现卓越性能。巴洛双生与DER的集成提升了数据效率，并在强化学习任务中取得了优越表现。BarlowRL展示了将自监督学习技术融入强化学习算法以提升其性能的潜力。