Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Pessimism for Robust and Efficient Off-Policy Reinforcement
  Learning   Popular off-policy deep reinforcement learning algorithms compensate for
overestimation bias during temporal-difference learning by utilizing
pessimistic estimates of the expected target returns. In this work, we propose
a novel learnable penalty to enact such pessimism, based on a new way to
quantify the critic's epistemic uncertainty. Furthermore, we propose to learn
the penalty alongside the critic with dual TD-learning, a strategy to estimate
and minimize the bias magnitude in the target returns. Our method enables us to
accurately counteract overestimation bias throughout training without incurring
the downsides of overly pessimistic targets. Empirically, by integrating our
method and other orthogonal improvements with popular off-policy algorithms, we
achieve state-of-the-art results in continuous control tasks from both
proprioceptive and pixel observations.

5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th