  When an agent encounters a continual stream of new tasks in the lifelong
learning setting, it leverages the knowledge it gained from the earlier tasks
to help learn the new tasks better. In such a scenario, identifying an
efficient knowledge representation becomes a challenging problem. Most research
works propose to either store a subset of examples from the past tasks in a
replay buffer, dedicate a separate set of parameters to each task or penalize
excessive updates over parameters by introducing a regularization term. While
existing methods employ the general task-agnostic stochastic gradient descent
update rule, we propose a task-aware optimizer that adapts the learning rate
based on the relatedness among tasks. We utilize the directions taken by the
parameters during the updates by accumulating the gradients specific to each
task. These task-based accumulated gradients act as a knowledge base that is
maintained and updated throughout the stream. We empirically show that our
proposed adaptive learning rate not only accounts for catastrophic forgetting
but also allows positive backward transfer. We also show that our method
performs better than several state-of-the-art methods in lifelong learning on
complex datasets with a large number of tasks.
