Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Bottleneck for Robust Self-Supervised Exploration   Exploration methods based on pseudo-count of transitions or curiosity of
dynamics have achieved promising results in solving reinforcement learning with
sparse rewards. However, such methods are usually sensitive to environmental
dynamics-irrelevant information, e.g., white-noise. To handle such
dynamics-irrelevant information, we propose a Dynamic Bottleneck (DB) model,
which attains a dynamics-relevant representation based on the
information-bottleneck principle. Based on the DB model, we further propose
DB-bonus, which encourages the agent to explore state-action pairs with high
information gain. We establish theoretical connections between the proposed
DB-bonus, the upper confidence bound (UCB) for linear case, and the visiting
count for tabular case. We evaluate the proposed method on Atari suits with
dynamics-irrelevant noises. Our experiments show that exploration with DB bonus
outperforms several state-of-the-art exploration methods in noisy environments.

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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg