de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Apprenticeship learning via soft local homomorphisms

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83823

Boularias,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Boularias, A. (2010). Apprenticeship learning via soft local homomorphisms. In 2010 IEEE International Conference on Robotics and Automation (ICRA 2010) (pp. 2971-2976). Piscataway, NJ, USA: IEEE.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C03A-5
Abstract
We consider the problem of apprenticeship learning when the expert's demonstration covers only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient solution to this problem based on the assumption that the expert is optimally acting in a Markov Decision Process (MDP). However, past work on IRL requires an accurate estimate of the frequency of encountering each feature of the states when the robot follows the expert‘s policy. Given that the complete policy of the expert is unknown, the features frequencies can only be empirically estimated from the demonstrated trajectories. In this paper, we propose to use a transfer method, known as soft homomorphism, in order to generalize the expert‘s policy to unvisited regions of the state space. The generalized policy can be used either as the robot‘s final policy, or to calculate the features frequencies within an IRL algorithm. Empirical results show that our approach is able to learn good policies from a small number of demonstrations.