结合CNN的可切换轻量级反对称处理（SLAP）以更小样本超越数据增强——在五子棋强化学习中的应用
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning
AI总结：
本文提出SLAP方法替代数据增强，通过强化经验加速机器学习并减少样本量，在五子棋CNN实验中收敛速度提升83%，样本量仅需八分之一。
摘要：
为了替代数据增强，本文提出了一种名为SLAP的方法，通过强化经验来加速机器学习并减少样本量。SLAP是一种独立于模型的协议/函数，能够在给定不同变换变体的情况下产生相同的输出。在使用五子棋游戏状态的实验中，SLAP将卷积神经网络学习的收敛速度提高了83%，且样本量仅为数据增强的八分之一。在五子棋的强化学习中，以使用数据增强的AlphaGo Zero/AlphaZero算法作为基线，SLAP将训练样本数量减少了8倍，并在对抗相同评估器时达到了相似的胜率，但尚无明确证据表明它能加速强化学习。该方法的益处至少应适用于对对称性或某些变换保持不变性的领域。作为未来工作，SLAP可能有助于对对称性不保持不变性的领域进行更具可解释性的学习和迁移学习，作为迈向通用人工智能的一小步。
英文摘要：
To replace data augmentation, this paper proposed a method called SLAP to intensify experience to speed up machine learning and reduce the sample size. SLAP is a model-independent protocol/function to produce the same output given different transformation variants. SLAP improved the convergence speed of convolutional neural network learning by 83% in the experiments with Gomoku game states, with only one eighth of the sample size compared with data augmentation. In reinforcement learning for Gomoku, using AlphaGo Zero/AlphaZero algorithm with data augmentation as baseline, SLAP reduced the number of training samples by a factor of 8 and achieved similar winning rate against the same evaluator, but it was not yet evident that it could speed up reinforcement learning. The benefits should at least apply to domains that are invariant to symmetry or certain transformations. As future work, SLAP may aid more explainable learning and transfer learning for domains that are not invariant to symmetry, as a small step towards artificial general intelligence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
