Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A reinforcement learning approach to hybrid control design   In this paper we design hybrid control policies for hybrid systems whose
mathematical models are unknown. Our contributions are threefold. First, we
propose a framework for modelling the hybrid control design problem as a single
Markov Decision Process (MDP). This result facilitates the application of
off-the-shelf algorithms from Reinforcement Learning (RL) literature towards
designing optimal control policies. Second, we model a set of benchmark
examples of hybrid control design problem in the proposed MDP framework. Third,
we adapt the recently proposed Proximal Policy Optimisation (PPO) algorithm for
the hybrid action space and apply it to the above set of problems. It is
observed that in each case the algorithm converges and finds the optimal
policy.

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The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th