  Our research is focused on understanding and applying biological memory
transfers to new AI systems that can fundamentally improve their performance,
throughout their fielded lifetime experience. We leverage current understanding
of biological memory transfer to arrive at AI algorithms for memory
consolidation and replay. In this paper, we propose the use of generative
memory that can be recalled in batch samples to train a multi-task agent in a
pseudo-rehearsal manner. We show results motivating the need for task-agnostic
separation of latent space for the generative memory to address issues of
catastrophic forgetting in lifelong learning.
