  We study the problem of fitting a model to a dynamical environment when new
modes of behavior emerge sequentially. The learning model is aware when a new
mode appears, but it cannot access the true modes of individual training
sequences. The state-of-the-art continual learning approaches cannot handle
this setup, because parameter transfer suffers from catastrophic interference
and episodic memory design requires the knowledge of the ground-truth modes of
sequences. We devise a novel continual learning method that overcomes both
limitations by maintaining a \textit{descriptor} of the mode of an encountered
sequence in a neural episodic memory. We employ a Dirichlet Process prior on
the attention weights of the memory to foster efficient storage of the mode
descriptors. Our method performs continual learning by transferring knowledge
across tasks by retrieving the descriptors of similar modes of past tasks to
the mode of a current sequence and feeding this descriptor into its transition
kernel as control input. We observe the continual learning performance of our
method to compare favorably to the mainstream parameter transfer approach.
