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2010
IEEE

Apprenticeship learning via soft local homomorphisms

8 years 9 months ago
Apprenticeship learning via soft local homomorphisms
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 l...
Abdeslam Boularias, Brahim Chaib-draa
Added 26 Jan 2011
Updated 26 Jan 2011
Type Journal
Year 2010
Where ICRA
Authors Abdeslam Boularias, Brahim Chaib-draa
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