isanunbiasedstochasticgradientdescentupdateruleforthefollowingempiricalrisk: R(θ) = X

This section contains the theoretical analysis of the loss functions of offline experience replay (Proposition 2),augmented experience replay (Proposition 3),andonline experience replay with reservoirsampling(Proposition1). For all experiments, we use the learning rate of 0.1 following the same setting as in Aljundi et al. [2019], Shimetal.[2021], This paper uses Randaugment [Cubuk et al., 2020], which is an auto augmentation method. It randomly selectsP augmentation operators from a set of 14 operators and applies them to the images. ToapplyBPGintheOCLenvironment,weproposeto determine the better/worse action set based on the feedback in the form of current memory batch accuracyAM,which reflects the memory overfitting level of the CL agent.