Relative Entropy Inverse Reinforcement Learning

2011

Conference Paper

ei

We consider the problem of imitation learning where the examples, demonstrated by an expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient tool for generalizing the demonstration, based on the assumption that the expert is optimally acting in a Markov Decision Process (MDP). Most of the past work on IRL requires that a (near)-optimal policy can be computed for different reward functions. However, this requirement can hardly be satisfied in systems with a large, or continuous, state space. In this paper, we propose a model-free IRL algorithm, where the relative entropy between the empirical distribution of the state-action trajectories under a uniform policy and their distribution under the learned policy is minimized by stochastic gradient descent. We compare this new approach to well-known IRL algorithms using approximate MDP models. Empirical results on simulated car racing, gridworld and ball-in-a-cup problems show that our approach is able to learn good policies from a small number of demonstrations.

@inproceedings{BoulariasKP2011,
  title = {Relative Entropy Inverse Reinforcement Learning},
  author = {Boularias, A. and Kober, J. and Peters, J.},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011},
  pages = {182-189},
  editors = {Gordon, G. , D. Dunson, M. Dudík },
  publisher = {MIT Press},
  address = {Cambridge, MA, USA},
  month = apr,
  year = {2011},
  doi = {},
  month_numeric = {4}
}