Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Batch-Constrained Reinforcement Learning for Dynamic Distribution
  Network Reconfiguration   Dynamic distribution network reconfiguration (DNR) algorithms perform hourly
status changes of remotely controllable switches to improve distribution system
performance. The problem is typically solved by physical model-based control
algorithms, which not only rely on accurate network parameters but also lack
scalability. To address these limitations, this paper develops a data-driven
batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR
problem. The proposed RL algorithm learns the network reconfiguration control
policy from a finite historical operational dataset without interacting with
the distribution network. The numerical study results on three distribution
networks show that the proposed algorithm not only outperforms state-of-the-art
RL algorithms but also improves the behavior control policy, which generated
the historical operational data. The proposed algorithm is also very scalable
and can find a desirable network reconfiguration solution in real-time.

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The categories of this paper are: astro-ph
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph