Proceedings

Bayesian Nonparametric Feature Construction for Inverse Reinforcement Learning

Jaedeug Choi, Kee-Eung Kim

Download BibTeX Cite

Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1287-1292.

Abstract

Most of the algorithms for inverse reinforcement learning (IRL) assume that the reward function is a linear function of the pre-defined state and action features. However, it is often difficult to manually specify the set of features that can make the true reward function representable as a linear function. We propose a Bayesian nonparametric approach to identifying useful composite features for learning the reward function. The composite features are assumed to be the logical conjunctions of the predefined atomic features so that we can represent the reward function as a linear function of the composite features. We empirically show that our approach is able to learn composite features that capture important aspects of the reward function on synthetic domains, and predict taxi drivers' behaviour with high accuracy on a real GPS trace dataset.

Jaedeug Choi, and Kee-Eung Kim. 2013. Bayesian Nonparametric Feature Construction for Inverse Reinforcement Learning. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 1287–1292. https://ijcai.org/proceedings/2013/194
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards