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Apprenticeship learning via soft local homomorphisms
2010 IEEE International Conference on Robotics and Automation
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 completedoi:10.1109/robot.2010.5509717 dblp:conf/icra/BoulariasC10 fatcat:43onzr4yivgb7baxtyy6mamb5e