Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Preprocessing Reward Functions for Interpretability   In many real-world applications, the reward function is too complex to be
manually specified. In such cases, reward functions must instead be learned
from human feedback. Since the learned reward may fail to represent user
preferences, it is important to be able to validate the learned reward function
prior to deployment. One promising approach is to apply interpretability tools
to the reward function to spot potential deviations from the user's intention.
Existing work has applied general-purpose interpretability tools to understand
learned reward functions. We propose exploiting the intrinsic structure of
reward functions by first preprocessing them into simpler but equivalent reward
functions, which are then visualized. We introduce a general framework for such
reward preprocessing and propose concrete preprocessing algorithms. Our
empirical evaluation shows that preprocessed rewards are often significantly
easier to understand than the original reward.

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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
32
The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg