In order to improve the effect of sports movement training, this paper builds a sports movement training model based on artificial intelligence technology based on the generation of confrontation network model. Moreover, in order to achieve the combination of model and model-free deep reinforcementlearning algorithm, this paper implements the model’s guidance and constraints on deep reinforcement learning algorithm from the perspective of reward value and behavior strategy and divides the model into two situations. In one case, the existing or manually established expert rules are used as model constraints, which is equivalent to online learning by experts. In another case, expert samples are used as model constraints, and an imitation learning method based on generative adversarial networks is introduced. Moreover, using expert samples as training data, the mechanism that the model is guided by the reward value is combined with the model-free algorithm by generating a confrontation network structure. Finally, this paper studies the performance of the model through experimental research. The research results show that the model constructed in this paper has a certain effect.

中文翻译：

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基于生成对抗网络模型和人工智能的运动动作训练方法研究

为了提高运动训练的效果，在对抗网络模型生成的基础上，建立了基于人工智能技术的运动训练模型。此外，为了实现模型与无模型的深度强化学习算法的结合，从奖励价值和行为策略的角度实现了模型对深度强化学习算法的指导和约束，并将模型分为两种情况。在一种情况下，将现有的或手动建立的专家规则用作模型约束，等效于专家在线学习。在另一种情况下，将专家样本用作模型约束，并介绍了一种基于生成对抗网络的模仿学习方法。而且，使用专家样本作为训练数据，通过生成对抗网络结构，将奖励值指导模型的机制与无模型算法相结合。最后，本文通过实验研究了该模型的性能。研究结果表明，本文构建的模型具有一定的效果。