Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptable Automation with Modular Deep Reinforcement Learning and Policy
  Transfer   Recent advances in deep Reinforcement Learning (RL) have created
unprecedented opportunities for intelligent automation, where a machine can
autonomously learn an optimal policy for performing a given task. However,
current deep RL algorithms predominantly specialize in a narrow range of tasks,
are sample inefficient, and lack sufficient stability, which in turn hinder
their industrial adoption. This article tackles this limitation by developing
and testing a Hyper-Actor Soft Actor-Critic (HASAC) RL framework based on the
notions of task modularization and transfer learning. The goal of the proposed
HASAC is to enhance the adaptability of an agent to new tasks by transferring
the learned policies of former tasks to the new task via a "hyper-actor". The
HASAC framework is tested on a new virtual robotic manipulation benchmark,
Meta-World. Numerical experiments show superior performance by HASAC over
state-of-the-art deep RL algorithms in terms of reward value, success rate, and
task completion time.

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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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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: supr-con