Relative Entropy Inverse Reinforcement Learning
2011
Conference Paper
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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.
Author(s): | Boularias, A. and Kober, J. and Peters, J. |
Book Title: | JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011 |
Pages: | 182-189 |
Year: | 2011 |
Month: | April |
Day: | 0 |
Editors: | Gordon, G. , D. Dunson, M. Dudík |
Publisher: | MIT Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Fourteenth International Conference on Artificial Intelligence and Statistics |
Event Place: | Ft. Lauderdale, FL, USA |
Address: | Cambridge, MA, USA |
Digital: | 0 |
Links: |
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BibTex @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} } |