Assisted therapy is increasingly used in autism spectrum disorders (ASD) for improving social interaction and communication skills in recent years. A lot of studies have proven that the form of interactive games for therapy has a good effect on children with autism. Thus, our study provided an assisted therapeutic system based on Reinforcement Learning (RL) for children with ASD, which has five interactive subgames. As is well known, it is necessary to establish and maintain compelling interactions in therapeutic process. Therefore, we aim to adjust the interactive content according to the emotions of children with autism. However, due to the atypical and unusually differences in children with autism, most systems are based on off-line training of small samples of individuals and online recognition, so the existing assisted systems are limited in their ability to automatically update system parameters of new mappings. The integration of RL and Convolutional Neural Network (CNN)-Support Vector Regression (SVR) was used to deal with the updating online of prediction model’s weights. The normalized emotion labels were evaluated by the therapists. Eleven children with autism as subjects were invited in this experiment and captured facial video images. The experiment lasted for five weeks of intermittent assisted therapy, and the results were evaluated for the system and the therapy effect. Finally, we achieved a general reduction in the root mean square error of the model prediction results and labels. Although there is no significant difference in Social Responsiveness Scale (SRS) scores before and after assisted therapy (p value = 0.60), in individual subjects (Sub. 1, Sub. 2 and Sub.3), the SRS total score is significantly reduced (Average drop of 19 points). These results demonstrate the effectiveness of prediction model based on RL and show the feasibility of assisted therapeutic system in children with autism.

近年来，辅助疗法越来越多地用于自闭症谱系障碍（ASD）中，以改善社交互动和沟通技巧。许多研究证明，互动游戏形式的治疗对自闭症儿童具有良好的效果。因此，我们的研究为ASD儿童提供了基于强化学习（RL）的辅助治疗系统，该系统具有五个互动子游戏。众所周知，在治疗过程中必须建立和维持令人信服的相互作用。因此，我们旨在根据自闭症儿童的情绪来调整互动内容。但是，由于自闭症儿童的非典型性和非同寻常的差异，大多数系统都基于对少量个体样本的离线训练和在线识别，因此，现有辅助系统的自动更新新映射的系统参数的能力受到限制。RL和卷积神经网络（CNN）-支持向量回归（SVR）的集成用于处理预测模型权重的在线更新。归一化的情绪标签由治疗师评估。该实验邀请了11名自闭症儿童参加，并拍摄了面部视频图像。实验持续了五周的间歇辅助治疗，并评估了系统和治疗效果的结果。最后，我们总体上降低了模型预测结果和标签的均方根误差。尽管辅助治疗前后的社会反应量表（SRS）得分没有显着差异（p值= 0.60），在个体受试者（Sub.1，Sub.2和Sub.3）中，SRS的总得分显着降低（平均下降19分）。这些结果证明了基于RL的预测模型的有效性，并证明了自闭症儿童辅助治疗系统的可行性。