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11

ICMLA
2008

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A Predictive Model for Imitation Learning in Partially Observable Environments

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A Predictive Model for Imitation Learning in Partially Observable Environments

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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

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Assistive Robot User | ICMLA 2008 | Machine Learning | Model | Predictive Policy Representation |

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Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2008
Where	ICMLA
Authors	Abdeslam Boularias

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