@InProceedings{Supelec797,
author = {Edouard Klein and Matthieu Geist and Bilal PIOT and Olivier Pietquin},
title = {{Inverse Reinforcement Learning through Structured Classification}},
year = {2012},
booktitle = {{Advances in Neural Information Processing Systems (NIPS 2012)}},
month = {December},
address = {Lake Tahoe (NV, USA)},
url = {http://papers.nips.cc/paper/4551-inverse-reinforcement-learning-through-structured-classification.pdf},
abstract = {This paper adresses the inverse reinforcement learning (IRL)
problem, that is inferring a reward for which a demonstrated
expert behavior is optimal. We introduce a new algorithm,
SCIRL, whose principle is to use the so-called feature
expectation of the expert as the parameterization of the score
function of a multiclass classifier. This approach produces a
reward function for which the expert policy is provably near-
optimal. Contrary to most of existing IRL algorithms, SCIRL
does not require solving a single time the direct RL problem.
Moreover, up to the use of some heuristic, it may work with
only trajectories sampled according to the expert behavior.
This is illustrated on a car driving simulator.}
}