  Learning policies that generalize across multiple tasks is an important and
challenging research topic in reinforcement learning and robotics. Training
individual policies for every single potential task is often impractical,
especially for continuous task variations, requiring more principled approaches
to share and transfer knowledge among similar tasks. We present a novel
approach for learning a nonlinear feedback policy that generalizes across
multiple tasks. The key idea is to define a parametrized policy as a function
of both the state and the task, which allows learning a single policy that
generalizes across multiple known and unknown tasks. Applications of our novel
approach to reinforcement and imitation learning in real-robot experiments are
shown.
