Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Constrained Variational Policy Optimization for Safe Reinforcement
  Learning   Safe reinforcement learning (RL) aims to learn policies that satisfy certain
constraints before deploying to safety-critical applications. Primal-dual as a
prevalent constrained optimization framework suffers from instability issues
and lacks optimality guarantees. This paper overcomes the issues from a novel
probabilistic inference perspective and proposes an Expectation-Maximization
style approach to learn safe policy. We show that the safe RL problem can be
decomposed to 1) a convex optimization phase with a non-parametric variational
distribution and 2) a supervised learning phase. We show the unique advantages
of constrained variational policy optimization by proving its optimality and
policy improvement stability. A wide range of experiments on continuous robotic
tasks show that the proposed method achieves significantly better performance
in terms of constraint satisfaction and sample efficiency than primal-dual
baselines.

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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: stat