Closing the learning-planning loop with predictive state representations
نویسندگان
چکیده
منابع مشابه
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of our environment, and then plan to maximize reward. Unfortunately, learning algorithms often recover a model which is too inaccurate to support planning or too large and complex for planning to be feasible; or, they re...
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Over the past decade there has been considerable interest in spectral algorithms for learning Predictive State Representations (PSRs). Spectral algorithms have appealing theoretical guarantees; however, the resulting models do not always perform well on inference tasks in practice. One reason for this behavior is the mismatch between the intended task (accurate filtering or prediction) and the ...
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ژورنال
عنوان ژورنال: The International Journal of Robotics Research
سال: 2011
ISSN: 0278-3649,1741-3176
DOI: 10.1177/0278364911404092