  Methods for learning from demonstration (LfD) have shown success in acquiring
behavior policies by imitating a user. However, even for a single task, LfD may
require numerous demonstrations. For versatile agents that must learn many
tasks via demonstration, this process would substantially burden the user if
each task were learned in isolation. To address this challenge, we introduce
the novel problem of lifelong learning from demonstration, which allows the
agent to continually build upon knowledge learned from previously demonstrated
tasks to accelerate the learning of new tasks, reducing the amount of
demonstrations required. As one solution to this problem, we propose the first
lifelong learning approach to inverse reinforcement learning, which learns
consecutive tasks via demonstration, continually transferring knowledge between
tasks to improve performance.
