Continual Learning through Evolvable Neural Turing Machines
Benno Lüders, Mikkel Schläger, Sebastian Risi · IT University Of Copenhagen (IT University of Copenhagen) · 2016
Continual learning, i.e. the ability to sequentially learn tasks without catastrophic forgetting of previously learned ones, is an important open challenge in machine learning. In this paper we take a step in this direction by showing that the recently proposed Evolving Neural Turing Machine (ENTM) approach is able to perform one-shot learning in a reinforcement learning task without catastrophic forgetting of previously stored associations.