  This paper proposes an assessor-guided learning strategy for continual
learning where an assessor guides the learning process of a base learner by
controlling the direction and pace of the learning process thus allowing an
efficient learning of new environments while protecting against the
catastrophic interference problem. The assessor is trained in a meta-learning
manner with a meta-objective to boost the learning process of the base learner.
It performs a soft-weighting mechanism of every sample accepting positive
samples while rejecting negative samples. The training objective of a base
learner is to minimize a meta-weighted combination of the cross entropy loss
function, the dark experience replay (DER) loss function and the knowledge
distillation loss function whose interactions are controlled in such a way to
attain an improved performance. A compensated over-sampling (COS) strategy is
developed to overcome the class imbalanced problem of the episodic memory due
to limited memory budgets. Our approach, Assessor-Guided Learning Approach
(AGLA), has been evaluated in the class-incremental and task-incremental
learning problems. AGLA achieves improved performances compared to its
competitors while the theoretical analysis of the COS strategy is offered.
Source codes of AGLA, baseline algorithms and experimental logs are shared
publicly in \url{https://github.com/anwarmaxsum/AGLA} for further study.
