A Predictive Model for Imitation Learning in Partially Observable Environments

Boularias, A; Wani,; A., M.; Chen, X.-W.; Casasent, D.; Kurgan, L. A.; Hu, T.; Hafeez, K.

doi:10.1109/ICMLA.2008.142

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A Predictive Model for Imitation Learning in Partially Observable Environments

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Boularias, A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Boularias, A. (2008). A Predictive Model for Imitation Learning in Partially Observable Environments. Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA 2008), 83-90.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C629-C

Abstract

Learning by imitation has shown to be a powerful paradigm for automated learning in autonomous robots. This paper presents a general framework of learning by imitation for stochastic and partially observable systems. The model is a Predictive Policy Representation (PPR) whose goal is to represent the teacher‘s policies without any reference to states. The model is fully described in terms of actions and observations only. We show how this model can efficiently learn the personal behavior and preferences of an assistive robot user.