  Lifelong reinforcement learning provides a promising framework for developing
versatile agents that can accumulate knowledge over a lifetime of experience
and rapidly learn new tasks by building upon prior knowledge. However, current
lifelong learning methods exhibit non-vanishing regret as the amount of
experience increases and include limitations that can lead to suboptimal or
unsafe control policies. To address these issues, we develop a lifelong policy
gradient learner that operates in an adversarial set- ting to learn multiple
tasks online while enforcing safety constraints on the learned policies. We
demonstrate, for the first time, sublinear regret for lifelong policy search,
and validate our algorithm on several benchmark dynamical systems and an
application to quadrotor control.
