Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Attentional Credit Assignment for Transfer in Reinforcement
  Learning   The ability to transfer knowledge to novel environments and tasks is a
sensible desiderata for general learning agents. Despite the apparent promises,
transfer in RL is still an open and little exploited research area. In this
paper, we take a brand-new perspective about transfer: we suggest that the
ability to assign credit unveils structural invariants in the tasks that can be
transferred to make RL more sample-efficient. Our main contribution is SECRET,
a novel approach to transfer learning for RL that uses a backward-view credit
assignment mechanism based on a self-attentive architecture. Two aspects are
key to its generality: it learns to assign credit as a separate offline
supervised process and exclusively modifies the reward function. Consequently,
it can be supplemented by transfer methods that do not modify the reward
function and it can be plugged on top of any RL algorithm.

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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg