Life-long learning cell structures —continuously learning without catastrophic interference

We present a new method capable of learning multiple categories in an interactive and life-long learning fashion to approach the "stability-plasticity dilemma". The problem of incremental learning of multiple categories is still largely unsolved. This ...

In a multi-task reinforcement learning setting, the learner commonly benefits from training on multiple related tasks by exploiting similarities among them. At the same time, the trained agent is able to solve a wider range of different problems. ...

Natural systems such as plants, animals and humans exhibit behaviour that forms distinct, rhythmic cycles. These cycles permit individuals and societies to learn, adapt and evolve in complex, dynamic environments. This paper introduces a model of ...