Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Keep Doing What Worked: Behavioral Modelling Priors for Offline
  Reinforcement Learning   Off-policy reinforcement learning algorithms promise to be applicable in
settings where only a fixed data-set (batch) of environment interactions is
available and no new experience can be acquired. This property makes these
algorithms appealing for real world problems such as robot control. In
practice, however, standard off-policy algorithms fail in the batch setting for
continuous control. In this paper, we propose a simple solution to this
problem. It admits the use of data generated by arbitrary behavior policies and
uses a learned prior -- the advantage-weighted behavior model (ABM) -- to bias
the RL policy towards actions that have previously been executed and are likely
to be successful on the new task. Our method can be seen as an extension of
recent work on batch-RL that enables stable learning from conflicting
data-sources. We find improvements on competitive baselines in a variety of RL
tasks -- including standard continuous control benchmarks and multi-task
learning for simulated and real-world robots.

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The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: supr-con