Relative Entropy of Correct Proximal Policy Optimization Algorithms with Modified Penalty Factor in Complex Environment.

Weimin Chen, Kelvin Kian Loong Wong, Sifan Long, Zhili Sun

Abstract read

In one paragraph

Article in Entropy (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

4 authors.

Weimin ChenSchool of Information and Electronics, Hunan City University, Yiyang 413000, China.

Kelvin Kian Loong WongSchool of Information and Electronics, Hunan City University, Yiyang 413000, China.

Sifan LongSchool of Computer Science and Engineering, Central South University, Changsha 410075, China.ORCID 0000-0003-4450-5345

Zhili Sun5G&6G Innovation Centre, Department of Electrical and Electronic Engineering, Institute for Communication Systems, University of Surrey, Guildford GU2 7XH, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the field of reinforcement learning, we propose a Correct Proximal Policy Optimization (CPPO) algorithm based on the modified penalty factor

Indexed as

approximation theorycorrect proximal policy optimizationentropyoptimizationpolicy gradientreinforcement learning

Identifiers

PMID35455103

DOI10.3390/e24040440

PMCPMC9031020

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

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.