Towards Deep Reinforcement Learning Based Chinese Calligraphy Robot

Learning how to write Chinese character strokes from the stroke images directly, has a great significance to the inheritance of calligraphy art and to imitate the writing style of Chinese calligraphers. However, most of the existing methods directly applied existing samples with action labels. The performance of these methods is often limited by the quality and number of samples. Thus, these methods cannot be used to learn calligraphy from unlabeled samples. To address this problem, a calligraphy robotic model based on deep reinforcement learning is proposed in this paper, which enables a robotic arm to write fundamental Chinese character strokes from stroke images. In the model, writing task is seen as the process of interaction between the robot and the environment. The robot makes appropriate writing action based on the state information provided by the environment. In order to evaluate the writing action of the robot, a reward function is designed on the model. In addition, the stochastic policy gradient method is used in training on the model. Finally, the model was extensively experimented on a stroke data set. Environmental results demonstrate that the proposed model allows a calligraphy robot to successfully write fundamental Chinese character strokes from stroke images. This model provides a promising solution for reconstructing writing actions from images.