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 reinforcement learning 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.
An Enhanced Model-Free Reinforcement Learning Algorithm to Solve Nash Equilibrium for Multi-Agent Cooperative Game Systems
