一种基于条件生成对抗网络的强化学习数据增强方法OACSTPCD

More and more attention has been paid to the success of reinforcement learning in sequential decision making,but there is still a problem of low data efficiency when using high-dimensional state as input.One of the reasons for this problem is that it is difficult for an agent to extract effective features from a high-dimensional space.In order to improve data efficiency,this paper proposes a data augmentation method cGDA(cGANs-based Data Augment)suitable for reinforcement learning task.Conditional generative adversarial nets(cGANs)is used to model the dynamic characteristics of the environment,with the state and action at the current moment as the input of the conditional generation model.The model outputs the state of the next moment as augmented data.In the process of training,real data and augmented data are used to train agents,which can effectively help agents to extract useful knowledge from different data quickly.On the Atari100K benchmark,cGDA achieves higher performance in 16 of 26 discrete control problem environments compared with the methods with data augmentation.Higher performance is achieved in 14 environ-ments compared with the approach without data augmentation.