Catastrophic forgetting has been a significant problem hindering the deployment of deep learning algorithms in continual setting. Numerous methods have proposed to address catastrophic where an agent loses its generalization power old tasks while new tasks. We put forward alternative strategy handle with knowledge amalgamation (CFA), which learns student network from multiple heterogeneous teac...

This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using optimal weight initialization learned from first solving a similar easier envi...

This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We show a set of experiments that outlines some prel...