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arXiv · 1608.00737

Context Discovery for Model Learning in Partially Observable Environments

Nikolas J. Hemion
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Abstract

The ability to learn a model is essential for the success of autonomous agents. Unfortunately, learning a model is difficult in partially observable environments, where latent environmental factors influence what the agent observes. In the absence of a supervisory training signal, autonomous agents therefore require a mechanism to autonomously discover these environmental factors, or sensorimotor contexts. This paper presents a method to discover sensorimotor contexts in partially observable environments, by constructing a hierarchical transition model. The method is evaluated in a simulation experiment, in which a robot learns that different rooms are characterized by different objects that are found in them.

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Nikolas J. Hemion. 2016-08-02. Context Discovery for Model Learning in Partially Observable Environments. https://arxiv.org/abs/1608.00737

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