Multi-agents Rounding Strategy based on DeepReinforcement Learning GCMSA Algorithm Article Swipe
Zhaotian Wei
,
Ruixuan Wei
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4504024/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4504024/v1
Aiming at the problem of target rounding by multi-agents in complex environ-ments, this paper proposes a Goal Consistency Reinforcement Learning Approachbased on Multi-head Soft Attention(GCMSA). Firstly, in order to make the modelcloser to reality, the reward function when the target is at different positions andthe target escape strategy are set respectively. Then, the Multi-head soft atten-tion module is used to promote the information cognition of the target amongthe agents, so that the agents can complete the target roundup more smoothly.Finally, in the training phase, this paper introduces cognitive dissonance loss toimprove the sample utilisation. Simulation experiments show that GCMSA isable to obtain a higher task success rate and significantly better than MADDPGin terms of algorithm performance.
Related Topics
Concepts
Cognitive dissonance
Rounding
Reinforcement learning
Computer science
Task (project management)
Consistency (knowledge bases)
Set (abstract data type)
Cognition
Artificial intelligence
Function (biology)
Algorithm
Mathematical optimization
Mathematics
Engineering
Psychology
Social psychology
Evolutionary biology
Operating system
Programming language
Biology
Systems engineering
Neuroscience
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4504024/v1
- https://www.researchsquare.com/article/rs-4504024/latest.pdf
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401117421
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401117421Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-4504024/v1Digital Object Identifier
- Title
-
Multi-agents Rounding Strategy based on DeepReinforcement Learning GCMSA AlgorithmWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-30Full publication date if available
- Authors
-
Zhaotian Wei, Ruixuan WeiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-4504024/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-4504024/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-4504024/latest.pdfDirect OA link when available
- Concepts
-
Cognitive dissonance, Rounding, Reinforcement learning, Computer science, Task (project management), Consistency (knowledge bases), Set (abstract data type), Cognition, Artificial intelligence, Function (biology), Algorithm, Mathematical optimization, Mathematics, Engineering, Psychology, Social psychology, Evolutionary biology, Operating system, Programming language, Biology, Systems engineering, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4401117421 |
|---|---|
| doi | https://doi.org/10.21203/rs.3.rs-4504024/v1 |
| ids.doi | https://doi.org/10.21203/rs.3.rs-4504024/v1 |
| ids.openalex | https://openalex.org/W4401117421 |
| fwci | 0.0 |
| type | preprint |
| title | Multi-agents Rounding Strategy based on DeepReinforcement Learning GCMSA Algorithm |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10462 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Reinforcement Learning in Robotics |
| topics[1].id | https://openalex.org/T10249 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.995199978351593 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1705 |
| topics[1].subfield.display_name | Computer Networks and Communications |
| topics[1].display_name | Distributed Control Multi-Agent Systems |
| topics[2].id | https://openalex.org/T12794 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9947999715805054 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1703 |
| topics[2].subfield.display_name | Computational Theory and Mathematics |
| topics[2].display_name | Adaptive Dynamic Programming Control |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C77513098 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8023165464401245 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q209722 |
| concepts[0].display_name | Cognitive dissonance |
| concepts[1].id | https://openalex.org/C136625980 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7922053337097168 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q663208 |
| concepts[1].display_name | Rounding |
| concepts[2].id | https://openalex.org/C97541855 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7110822796821594 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q830687 |
| concepts[2].display_name | Reinforcement learning |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6677024960517883 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C2780451532 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5772247314453125 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[4].display_name | Task (project management) |
| concepts[5].id | https://openalex.org/C2776436953 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5573103427886963 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5163215 |
| concepts[5].display_name | Consistency (knowledge bases) |
| concepts[6].id | https://openalex.org/C177264268 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5124312043190002 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[6].display_name | Set (abstract data type) |
| concepts[7].id | https://openalex.org/C169900460 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4747201204299927 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2200417 |
| concepts[7].display_name | Cognition |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4508034586906433 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C14036430 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4474937915802002 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3736076 |
| concepts[9].display_name | Function (biology) |
| concepts[10].id | https://openalex.org/C11413529 |
| concepts[10].level | 1 |
| concepts[10].score | 0.37838664650917053 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[10].display_name | Algorithm |
| concepts[11].id | https://openalex.org/C126255220 |
| concepts[11].level | 1 |
| concepts[11].score | 0.35039645433425903 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[11].display_name | Mathematical optimization |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.1770412027835846 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C127413603 |
| concepts[13].level | 0 |
| concepts[13].score | 0.14467379450798035 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[13].display_name | Engineering |
| concepts[14].id | https://openalex.org/C15744967 |
| concepts[14].level | 0 |
| concepts[14].score | 0.11286067962646484 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[14].display_name | Psychology |
| concepts[15].id | https://openalex.org/C77805123 |
| concepts[15].level | 1 |
| concepts[15].score | 0.06702491641044617 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q161272 |
| concepts[15].display_name | Social psychology |
| concepts[16].id | https://openalex.org/C78458016 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q840400 |
| concepts[16].display_name | Evolutionary biology |
| concepts[17].id | https://openalex.org/C111919701 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[17].display_name | Operating system |
| concepts[18].id | https://openalex.org/C199360897 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[18].display_name | Programming language |
| concepts[19].id | https://openalex.org/C86803240 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[19].display_name | Biology |
| concepts[20].id | https://openalex.org/C201995342 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q682496 |
| concepts[20].display_name | Systems engineering |
| concepts[21].id | https://openalex.org/C169760540 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[21].display_name | Neuroscience |
| keywords[0].id | https://openalex.org/keywords/cognitive-dissonance |
| keywords[0].score | 0.8023165464401245 |
| keywords[0].display_name | Cognitive dissonance |
| keywords[1].id | https://openalex.org/keywords/rounding |
| keywords[1].score | 0.7922053337097168 |
| keywords[1].display_name | Rounding |
| keywords[2].id | https://openalex.org/keywords/reinforcement-learning |
| keywords[2].score | 0.7110822796821594 |
| keywords[2].display_name | Reinforcement learning |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6677024960517883 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/task |
| keywords[4].score | 0.5772247314453125 |
| keywords[4].display_name | Task (project management) |
| keywords[5].id | https://openalex.org/keywords/consistency |
| keywords[5].score | 0.5573103427886963 |
| keywords[5].display_name | Consistency (knowledge bases) |
| keywords[6].id | https://openalex.org/keywords/set |
| keywords[6].score | 0.5124312043190002 |
| keywords[6].display_name | Set (abstract data type) |
| keywords[7].id | https://openalex.org/keywords/cognition |
| keywords[7].score | 0.4747201204299927 |
| keywords[7].display_name | Cognition |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.4508034586906433 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/function |
| keywords[9].score | 0.4474937915802002 |
| keywords[9].display_name | Function (biology) |
| keywords[10].id | https://openalex.org/keywords/algorithm |
| keywords[10].score | 0.37838664650917053 |
| keywords[10].display_name | Algorithm |
| keywords[11].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[11].score | 0.35039645433425903 |
| keywords[11].display_name | Mathematical optimization |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.1770412027835846 |
| keywords[12].display_name | Mathematics |
| keywords[13].id | https://openalex.org/keywords/engineering |
| keywords[13].score | 0.14467379450798035 |
| keywords[13].display_name | Engineering |
| keywords[14].id | https://openalex.org/keywords/psychology |
| keywords[14].score | 0.11286067962646484 |
| keywords[14].display_name | Psychology |
| keywords[15].id | https://openalex.org/keywords/social-psychology |
| keywords[15].score | 0.06702491641044617 |
| keywords[15].display_name | Social psychology |
| language | en |
| locations[0].id | doi:10.21203/rs.3.rs-4504024/v1 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.researchsquare.com/article/rs-4504024/latest.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.21203/rs.3.rs-4504024/v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5064334850 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2810-9839 |
| authorships[0].author.display_name | Zhaotian Wei |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210104252 |
| authorships[0].affiliations[0].raw_affiliation_string | Graduate College, Air Force Engineering University, No. 1 Baling Road, Baqiao District, Xi'an, 710051, Shann'xi Province, China. |
| authorships[0].institutions[0].id | https://openalex.org/I4210104252 |
| authorships[0].institutions[0].ror | https://ror.org/00seraz22 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210104252 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Air Force Engineering University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zhaotian Wei |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Graduate College, Air Force Engineering University, No. 1 Baling Road, Baqiao District, Xi'an, 710051, Shann'xi Province, China. |
| authorships[1].author.id | https://openalex.org/A5078893680 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-1459-8122 |
| authorships[1].author.display_name | Ruixuan Wei |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210104252 |
| authorships[1].affiliations[0].raw_affiliation_string | Aviation Engineering School,, Air Force Engineering University, No. 1 Baling Road, Baqiao District, Xi'an, 710038, Shann'xi Province, China. |
| authorships[1].institutions[0].id | https://openalex.org/I4210104252 |
| authorships[1].institutions[0].ror | https://ror.org/00seraz22 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210104252 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Air Force Engineering University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Ruixuan Wei |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Aviation Engineering School,, Air Force Engineering University, No. 1 Baling Road, Baqiao District, Xi'an, 710038, Shann'xi Province, China. |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.researchsquare.com/article/rs-4504024/latest.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Multi-agents Rounding Strategy based on DeepReinforcement Learning GCMSA Algorithm |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10462 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Reinforcement Learning in Robotics |
| related_works | https://openalex.org/W3208334015, https://openalex.org/W2767403522, https://openalex.org/W2258133373, https://openalex.org/W2766249179, https://openalex.org/W2255937062, https://openalex.org/W1974328274, https://openalex.org/W1515826403, https://openalex.org/W4310034585, https://openalex.org/W1509473192, https://openalex.org/W2225059996 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21203/rs.3.rs-4504024/v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.researchsquare.com/article/rs-4504024/latest.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-4504024/v1 |
| primary_location.id | doi:10.21203/rs.3.rs-4504024/v1 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.researchsquare.com/article/rs-4504024/latest.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-4504024/v1 |
| publication_date | 2024-07-30 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2626637010, https://openalex.org/W2617547828, https://openalex.org/W2145339207, https://openalex.org/W4297815884, https://openalex.org/W2408768628, https://openalex.org/W2950993296, https://openalex.org/W2949963774, https://openalex.org/W4289100326, https://openalex.org/W4299802797, https://openalex.org/W4285605699, https://openalex.org/W2756196406, https://openalex.org/W2964338167, https://openalex.org/W4226062722, https://openalex.org/W2894976951, https://openalex.org/W3216363472, https://openalex.org/W4288336223, https://openalex.org/W4288601262, https://openalex.org/W2105850748, https://openalex.org/W4302565044, https://openalex.org/W2050044094, https://openalex.org/W4294656596, https://openalex.org/W3100930738, https://openalex.org/W2329356151, https://openalex.org/W4295598622, https://openalex.org/W2995937146 |
| referenced_works_count | 25 |
| abstract_inverted_index.a | 16, 103 |
| abstract_inverted_index.at | 2, 42 |
| abstract_inverted_index.by | 8 |
| abstract_inverted_index.in | 10, 27, 81 |
| abstract_inverted_index.is | 41, 58 |
| abstract_inverted_index.of | 5, 65, 114 |
| abstract_inverted_index.on | 22 |
| abstract_inverted_index.so | 70 |
| abstract_inverted_index.to | 29, 33, 60, 101 |
| abstract_inverted_index.and | 108 |
| abstract_inverted_index.are | 49 |
| abstract_inverted_index.can | 74 |
| abstract_inverted_index.set | 50 |
| abstract_inverted_index.the | 3, 31, 35, 39, 53, 62, 66, 72, 76, 82, 92 |
| abstract_inverted_index.Goal | 17 |
| abstract_inverted_index.Soft | 24 |
| abstract_inverted_index.loss | 90 |
| abstract_inverted_index.make | 30 |
| abstract_inverted_index.more | 79 |
| abstract_inverted_index.rate | 107 |
| abstract_inverted_index.show | 97 |
| abstract_inverted_index.soft | 55 |
| abstract_inverted_index.task | 105 |
| abstract_inverted_index.than | 111 |
| abstract_inverted_index.that | 71, 98 |
| abstract_inverted_index.this | 13, 85 |
| abstract_inverted_index.used | 59 |
| abstract_inverted_index.when | 38 |
| abstract_inverted_index.GCMSA | 99 |
| abstract_inverted_index.Then, | 52 |
| abstract_inverted_index.order | 28 |
| abstract_inverted_index.paper | 14, 86 |
| abstract_inverted_index.terms | 113 |
| abstract_inverted_index.Aiming | 1 |
| abstract_inverted_index.agents | 73 |
| abstract_inverted_index.andthe | 45 |
| abstract_inverted_index.better | 110 |
| abstract_inverted_index.escape | 47 |
| abstract_inverted_index.higher | 104 |
| abstract_inverted_index.isable | 100 |
| abstract_inverted_index.module | 57 |
| abstract_inverted_index.obtain | 102 |
| abstract_inverted_index.phase, | 84 |
| abstract_inverted_index.reward | 36 |
| abstract_inverted_index.sample | 93 |
| abstract_inverted_index.target | 6, 40, 46, 67, 77 |
| abstract_inverted_index.agents, | 69 |
| abstract_inverted_index.complex | 11 |
| abstract_inverted_index.problem | 4 |
| abstract_inverted_index.promote | 61 |
| abstract_inverted_index.roundup | 78 |
| abstract_inverted_index.success | 106 |
| abstract_inverted_index.Firstly, | 26 |
| abstract_inverted_index.Learning | 20 |
| abstract_inverted_index.MADDPGin | 112 |
| abstract_inverted_index.amongthe | 68 |
| abstract_inverted_index.complete | 75 |
| abstract_inverted_index.function | 37 |
| abstract_inverted_index.proposes | 15 |
| abstract_inverted_index.reality, | 34 |
| abstract_inverted_index.rounding | 7 |
| abstract_inverted_index.strategy | 48 |
| abstract_inverted_index.training | 83 |
| abstract_inverted_index.algorithm | 115 |
| abstract_inverted_index.cognition | 64 |
| abstract_inverted_index.cognitive | 88 |
| abstract_inverted_index.different | 43 |
| abstract_inverted_index.positions | 44 |
| abstract_inverted_index.toimprove | 91 |
| abstract_inverted_index.Multi-head | 23, 54 |
| abstract_inverted_index.Simulation | 95 |
| abstract_inverted_index.atten-tion | 56 |
| abstract_inverted_index.dissonance | 89 |
| abstract_inverted_index.introduces | 87 |
| abstract_inverted_index.Consistency | 18 |
| abstract_inverted_index.experiments | 96 |
| abstract_inverted_index.information | 63 |
| abstract_inverted_index.modelcloser | 32 |
| abstract_inverted_index.multi-agents | 9 |
| abstract_inverted_index.performance. | 116 |
| abstract_inverted_index.utilisation. | 94 |
| abstract_inverted_index.Approachbased | 21 |
| abstract_inverted_index.Reinforcement | 19 |
| abstract_inverted_index.respectively. | 51 |
| abstract_inverted_index.significantly | 109 |
| abstract_inverted_index.environ-ments, | 12 |
| abstract_inverted_index.Attention(GCMSA). | 25 |
| abstract_inverted_index.smoothly.Finally, | 80 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5078893680 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I4210104252 |
| citation_normalized_percentile.value | 0.12308622 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |