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ICMLA
2008

A Predictive Model for Imitation Learning in Partially Observable Environments

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A Predictive Model for Imitation Learning in Partially Observable Environments
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.
Abdeslam Boularias
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ICMLA
Authors Abdeslam Boularias
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