Continual Learning of Multi-modal Dynamics with External Memory
This addresses the challenge of catastrophic interference in continual learning for multi-modal dynamics, which is incremental as it builds on existing memory-based methods.
The paper tackles the problem of continual learning in dynamical environments where new behavior modes emerge sequentially, without access to true mode labels, by proposing a method that uses neural episodic memory with a Dirichlet Process prior to store mode descriptors, resulting in favorable performance compared to parameter transfer approaches.
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.