  Knowledge transfer between tasks can improve the performance of learned
models, but requires an accurate estimate of the inter-task relationships to
identify the relevant knowledge to transfer. These inter-task relationships are
typically estimated based on training data for each task, which is inefficient
in lifelong learning settings where the goal is to learn each consecutive task
rapidly from as little data as possible. To reduce this burden, we develop a
lifelong learning method based on coupled dictionary learning that utilizes
high-level task descriptions to model the inter-task relationships. We show
that using task descriptors improves the performance of the learned task
policies, providing both theoretical justification for the benefit and
empirical demonstration of the improvement across a variety of learning
problems. Given only the descriptor for a new task, the lifelong learner is
also able to accurately predict a model for the new task through zero-shot
learning using the coupled dictionary, eliminating the need to gather training
data before addressing the task.
