Research on Differential Privacy Protection of Two-player Games Based on Reinforcement Learning
For the two-player game problem,on the basis of Q-learning algorithm,the state-value function is updated by using neural network parameter approximation,the adaptive gradient opti-mization algorithm is selected for parameter updating,and the behaviors of the two agents are regulated by the Nash equilibrium idea.At the same time,in order to improve the protection effect of the model,differential privacy protection is added to the results to ensure the security of the data in the process of the two-player games.Finally,the experimental results verify the usa-bility of the algorithm,which is able to train two agents to reach their respective target points stably after multiple rounds.
reinforcement learningdifferential privacytwo-player games
万方数据
维普
