Multi-Task Policy Search

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Title: Multi-Task Policy Search
Authors: Deisenroth, MP
Englert, P
Peters, J
Fox, D
Item Type: Report
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.
Issue Date: 31-Dec-2013
URI: http://hdl.handle.net/10044/1/11623
Copyright Statement: © 2013 The Authors
Notes: 8 pages, double column. IEEE International Conference on Robotics and Automation, 2014
Appears in Collections:Computing



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