Evidence map›Paper›PMID 36433518›Full record

ArticleSensors (Basel, Switzerland)2022

Optimal Greedy Control in Reinforcement Learning.

Alexander Gorobtsov, Oleg Sychev, Yulia Orlova, Evgeniy Smirnov, Olga Grigoreva, Alexander Bochkin, Marina Andreeva

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alexander GorobtsovHigher Mathematics Department, Volgograd State Technical University, Lenin Ave, 28, Volgograd 400005, Russia.ORCID 0000-0002-5458-2240
Oleg SychevSoftware Engineering Department, Volgograd State Technical University, Lenin Ave, 28, Volgograd 400005, Russia.ORCID 0000-0002-7296-2538
Yulia OrlovaSoftware Engineering Department, Volgograd State Technical University, Lenin Ave, 28, Volgograd 400005, Russia.ORCID 0000-0003-4854-7462
Evgeniy SmirnovHigher Mathematics Department, Volgograd State Technical University, Lenin Ave, 28, Volgograd 400005, Russia.ORCID 0000-0002-8382-5299
Olga GrigorevaHigher Mathematics Department, Volgograd State Technical University, Lenin Ave, 28, Volgograd 400005, Russia.ORCID 0000-0002-6268-9486
Alexander BochkinHigher Mathematics Department, Volgograd State Technical University, Lenin Ave, 28, Volgograd 400005, Russia.ORCID 0000-0001-5774-093X
Marina AndreevaHigher Mathematics Department, Volgograd State Technical University, Lenin Ave, 28, Volgograd 400005, Russia.ORCID 0000-0002-7816-3833

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We consider the problem of dimensionality reduction of state space in the variational approach to the optimal control problem, in particular, in the reinforcement learning method. The control problem is described by differential algebraic equations consisting of nonlinear differential equations and algebraic constraint equations interconnected with Lagrange multipliers. The proposed method is based on changing the Lagrange multipliers of one subset based on the Lagrange multipliers of another subset. We present examples of the application of the proposed method in robotics and vibration isolation in transport vehicles. The method is implemented in FRUND-a multibody system dynamics software package.

Indexed as

machine learningoptimal controlreinforcement learningroboticsvariational methods

Identifiers

PMID36433518
PMCPMC9698335

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.