Frequency-based Search-control in Dyna. (arXiv:2002.05822v1 [cs.LG])

Model-based reinforcement learning has been empirically demonstrated as a
successful strategy to improve sample efficiency. In particular, Dyna is an
elegant model-based architecture integrating learning and planning that
provides huge flexibility of using a model. One of the most important
components in Dyna is called search-control, which refers to the process of
generating state or state-action pairs from which we query the model to acquire
simulated experiences. Search-control is critical in improving learning
efficiency. In this work, we propose a simple and novel search-control strategy
by searching high frequency regions of the value function. Our main intuition
is built on Shannon sampling theorem from signal processing, which indicates
that a high frequency signal requires more samples to reconstruct. We
empirically show that a high frequency function is more difficult to
approximate. This suggests a search-control strategy: we should use states from
high frequency regions of the value function to query the model to acquire more
samples. We develop a simple strategy to locally measure the frequency of a
function by gradient and hessian norms, and provide theoretical justification
for this approach. We then apply our strategy to search-control in Dyna, and
conduct experiments to show its property and effectiveness on benchmark

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