Publication
Multi-task policy search for robotics
Marc Peter Deisenroth; Peter Englert; Jan Peters; Dieter Fox
In: 2014 IEEE International Conference on Robotics and Automation. IEEE International Conference on Robotics and Automation (ICRA-2014), May 31 - June 7, Hong Kong, China, Pages 3876-3881, IEEE, 2014.
Abstract
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.