Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN
Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku
Reinforcement Learning
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.Comment: Change title; 6 pages, 8 figure