  Humans learn all their life long. They accumulate knowledge from a sequence
of learning experiences and remember the essential concepts without forgetting
what they have learned previously. Artificial neural networks struggle to learn
similarly. They often rely on data rigorously preprocessed to learn solutions
to specific problems such as classification or regression. In particular, they
forget their past learning experiences if trained on new ones. Therefore,
artificial neural networks are often inept to deal with real-life settings such
as an autonomous-robot that has to learn on-line to adapt to new situations and
overcome new problems without forgetting its past learning-experiences.
Continual learning (CL) is a branch of machine learning addressing this type of
problem. Continual algorithms are designed to accumulate and improve knowledge
in a curriculum of learning-experiences without forgetting. In this thesis, we
propose to explore continual algorithms with replay processes. Replay processes
gather together rehearsal methods and generative replay methods. Generative
Replay consists of regenerating past learning experiences with a generative
model to remember them. Rehearsal consists of saving a core-set of samples from
past learning experiences to rehearse them later. The replay processes make
possible a compromise between optimizing the current learning objective and the
past ones enabling learning without forgetting in sequences of tasks settings.
We show that they are very promising methods for continual learning. Notably,
they enable the re-evaluation of past data with new knowledge and the
confrontation of data from different learning-experiences. We demonstrate their
ability to learn continually through unsupervised learning, supervised learning
and reinforcement learning tasks.
