Multi-Task Policy Search

Abstract — Learning policies that generalize across multiple tasks is an important and challenging research topic in rein-forcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled ap-proaches 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. I