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Barlow双胞胎自监督学习框架与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的性能优于DER及其对比学习对应方法CURL。BarlowRL通过强制信息扩散至整个空间，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，最终实现显著性能提升。将Barlow双胞胎与DER相结合，增强了数据利用效率，并在强化学习任务中取得了卓越表现。BarlowRL展示了融合自监督学习技术以改进强化学习算法的潜力。