Discovering Symbolic Policy for Building Control using Reinforcement Learning Article Swipe
Soo Kyung Kim
,
Chihyeon Song
,
Weizhe Chen
,
Jinkyoo Park
,
Saman Mostafavi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2023.10.1848
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2023.10.1848
We propose a learning framework for interpretable HVAC control in buildings using deep reinforcement learning (DRL). Our framework includes a data-driven surrogate environment to emulate building dynamics and a Deep Symbolic Policy for discovering interpretable control policies. We focus on maintaining the temperature within the desired range for occupant comfort. Our results show that the discovered symbolic policies are interpretable and perform well compared to standard DRL algorithms. Additionally, the discovered policies in surrogate models exhibit transferability to physics-based environments with minimal performance degradation.
Related Topics
Concepts
Reinforcement learning
Computer science
HVAC
Transferability
Focus (optics)
Control (management)
Range (aeronautics)
Artificial intelligence
Scheme (mathematics)
Machine learning
Engineering
Mathematics
Logit
Air conditioning
Optics
Mathematical analysis
Physics
Aerospace engineering
Mechanical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2023.10.1848
- OA Status
- diamond
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388903443
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4388903443Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.ifacol.2023.10.1848Digital Object Identifier
- Title
-
Discovering Symbolic Policy for Building Control using Reinforcement LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Soo Kyung Kim, Chihyeon Song, Weizhe Chen, Jinkyoo Park, Saman MostafaviList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ifacol.2023.10.1848Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.ifacol.2023.10.1848Direct OA link when available
- Concepts
-
Reinforcement learning, Computer science, HVAC, Transferability, Focus (optics), Control (management), Range (aeronautics), Artificial intelligence, Scheme (mathematics), Machine learning, Engineering, Mathematics, Logit, Air conditioning, Optics, Mathematical analysis, Physics, Aerospace engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4388903443 |
|---|---|
| doi | https://doi.org/10.1016/j.ifacol.2023.10.1848 |
| ids.doi | https://doi.org/10.1016/j.ifacol.2023.10.1848 |
| ids.openalex | https://openalex.org/W4388903443 |
| fwci | 0.0 |
| type | article |
| title | Discovering Symbolic Policy for Building Control using Reinforcement Learning |
| biblio.issue | 2 |
| biblio.volume | 56 |
| biblio.last_page | 1527 |
| biblio.first_page | 1522 |
| topics[0].id | https://openalex.org/T10121 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9997000098228455 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2215 |
| topics[0].subfield.display_name | Building and Construction |
| topics[0].display_name | Building Energy and Comfort Optimization |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C97541855 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8500470519065857 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q830687 |
| concepts[0].display_name | Reinforcement learning |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6158974170684814 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C122346748 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5622835159301758 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1798773 |
| concepts[2].display_name | HVAC |
| concepts[3].id | https://openalex.org/C61272859 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5505139231681824 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7834031 |
| concepts[3].display_name | Transferability |
| concepts[4].id | https://openalex.org/C192209626 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5136606097221375 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q190909 |
| concepts[4].display_name | Focus (optics) |
| concepts[5].id | https://openalex.org/C2775924081 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5092772245407104 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q55608371 |
| concepts[5].display_name | Control (management) |
| concepts[6].id | https://openalex.org/C204323151 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4991335868835449 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q905424 |
| concepts[6].display_name | Range (aeronautics) |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4984464645385742 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C77618280 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4336369037628174 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1155772 |
| concepts[8].display_name | Scheme (mathematics) |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4122553765773773 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.24454692006111145 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0927627682685852 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C140331021 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1868104 |
| concepts[12].display_name | Logit |
| concepts[13].id | https://openalex.org/C103742991 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q173725 |
| concepts[13].display_name | Air conditioning |
| concepts[14].id | https://openalex.org/C120665830 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[14].display_name | Optics |
| concepts[15].id | https://openalex.org/C134306372 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[15].display_name | Mathematical analysis |
| concepts[16].id | https://openalex.org/C121332964 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[16].display_name | Physics |
| concepts[17].id | https://openalex.org/C146978453 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[17].display_name | Aerospace engineering |
| concepts[18].id | https://openalex.org/C78519656 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q101333 |
| concepts[18].display_name | Mechanical engineering |
| keywords[0].id | https://openalex.org/keywords/reinforcement-learning |
| keywords[0].score | 0.8500470519065857 |
| keywords[0].display_name | Reinforcement learning |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6158974170684814 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/hvac |
| keywords[2].score | 0.5622835159301758 |
| keywords[2].display_name | HVAC |
| keywords[3].id | https://openalex.org/keywords/transferability |
| keywords[3].score | 0.5505139231681824 |
| keywords[3].display_name | Transferability |
| keywords[4].id | https://openalex.org/keywords/focus |
| keywords[4].score | 0.5136606097221375 |
| keywords[4].display_name | Focus (optics) |
| keywords[5].id | https://openalex.org/keywords/control |
| keywords[5].score | 0.5092772245407104 |
| keywords[5].display_name | Control (management) |
| keywords[6].id | https://openalex.org/keywords/range |
| keywords[6].score | 0.4991335868835449 |
| keywords[6].display_name | Range (aeronautics) |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.4984464645385742 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/scheme |
| keywords[8].score | 0.4336369037628174 |
| keywords[8].display_name | Scheme (mathematics) |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.4122553765773773 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.24454692006111145 |
| keywords[10].display_name | Engineering |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.0927627682685852 |
| keywords[11].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1016/j.ifacol.2023.10.1848 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2898405271 |
| locations[0].source.issn | 2405-8963, 2405-8971 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2405-8963 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | IFAC-PapersOnLine |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IFAC-PapersOnLine |
| locations[0].landing_page_url | https://doi.org/10.1016/j.ifacol.2023.10.1848 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5101765243 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0631-5148 |
| authorships[0].author.display_name | Soo Kyung Kim |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I173498003 |
| authorships[0].affiliations[0].raw_affiliation_string | Palo Alto Research Center, Inc. (PARC), Palo Alto, CA 94304 USA |
| authorships[0].institutions[0].id | https://openalex.org/I173498003 |
| authorships[0].institutions[0].ror | https://ror.org/0529fxt39 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I173498003, https://openalex.org/I4210132870 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Palo Alto Research Center |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Soo Kyung Kim |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Palo Alto Research Center, Inc. (PARC), Palo Alto, CA 94304 USA |
| authorships[1].author.id | https://openalex.org/A5004079705 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Chihyeon Song |
| authorships[1].countries | KR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I157485424 |
| authorships[1].affiliations[0].raw_affiliation_string | Korea Advanced Institute of Science and Technology, Daejeon, South Korea |
| authorships[1].institutions[0].id | https://openalex.org/I157485424 |
| authorships[1].institutions[0].ror | https://ror.org/05apxxy63 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I157485424 |
| authorships[1].institutions[0].country_code | KR |
| authorships[1].institutions[0].display_name | Korea Advanced Institute of Science and Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Chihyeon Song |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Korea Advanced Institute of Science and Technology, Daejeon, South Korea |
| authorships[2].author.id | https://openalex.org/A5103236436 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9068-4247 |
| authorships[2].author.display_name | Weizhe Chen |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I1174212 |
| authorships[2].affiliations[0].raw_affiliation_string | University of Southern California, Los Angeles, CA 90007 |
| authorships[2].institutions[0].id | https://openalex.org/I1174212 |
| authorships[2].institutions[0].ror | https://ror.org/03taz7m60 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I1174212 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of Southern California |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Weizhe Chen |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | University of Southern California, Los Angeles, CA 90007 |
| authorships[3].author.id | https://openalex.org/A5023509025 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2620-1479 |
| authorships[3].author.display_name | Jinkyoo Park |
| authorships[3].countries | KR |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I157485424 |
| authorships[3].affiliations[0].raw_affiliation_string | Korea Advanced Institute of Science and Technology, Daejeon, South Korea |
| authorships[3].institutions[0].id | https://openalex.org/I157485424 |
| authorships[3].institutions[0].ror | https://ror.org/05apxxy63 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I157485424 |
| authorships[3].institutions[0].country_code | KR |
| authorships[3].institutions[0].display_name | Korea Advanced Institute of Science and Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jinkyoo Park |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | Korea Advanced Institute of Science and Technology, Daejeon, South Korea |
| authorships[4].author.id | https://openalex.org/A5104087723 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Saman Mostafavi |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I173498003 |
| authorships[4].affiliations[0].raw_affiliation_string | Palo Alto Research Center, Inc. (PARC), Palo Alto, CA 94304 USA |
| authorships[4].institutions[0].id | https://openalex.org/I173498003 |
| authorships[4].institutions[0].ror | https://ror.org/0529fxt39 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I173498003, https://openalex.org/I4210132870 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Palo Alto Research Center |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Saman Mostafavi |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | Palo Alto Research Center, Inc. (PARC), Palo Alto, CA 94304 USA |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.ifacol.2023.10.1848 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Discovering Symbolic Policy for Building Control using Reinforcement Learning |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10121 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9997000098228455 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2215 |
| primary_topic.subfield.display_name | Building and Construction |
| primary_topic.display_name | Building Energy and Comfort Optimization |
| related_works | https://openalex.org/W2112866972, https://openalex.org/W2161221533, https://openalex.org/W4240233711, https://openalex.org/W2900606913, https://openalex.org/W4320003279, https://openalex.org/W2326910963, https://openalex.org/W3111008797, https://openalex.org/W4287552621, https://openalex.org/W593427938, https://openalex.org/W4376649626 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1016/j.ifacol.2023.10.1848 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2898405271 |
| best_oa_location.source.issn | 2405-8963, 2405-8971 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2405-8963 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | IFAC-PapersOnLine |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | IFAC-PapersOnLine |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.ifacol.2023.10.1848 |
| primary_location.id | doi:10.1016/j.ifacol.2023.10.1848 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2898405271 |
| primary_location.source.issn | 2405-8963, 2405-8971 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2405-8963 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IFAC-PapersOnLine |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IFAC-PapersOnLine |
| primary_location.landing_page_url | https://doi.org/10.1016/j.ifacol.2023.10.1848 |
| publication_date | 2023-01-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2402261827, https://openalex.org/W6628216014, https://openalex.org/W2972720556, https://openalex.org/W3095810316, https://openalex.org/W2114157443, https://openalex.org/W2137983211, https://openalex.org/W7046505727, https://openalex.org/W1983563831, https://openalex.org/W4318994128, https://openalex.org/W4285379638, https://openalex.org/W6773942789, https://openalex.org/W6804601995, https://openalex.org/W2912904224, https://openalex.org/W2119717200, https://openalex.org/W3216772467, https://openalex.org/W4287996090, https://openalex.org/W4285262061 |
| referenced_works_count | 17 |
| abstract_inverted_index.a | 2, 19, 28 |
| abstract_inverted_index.We | 0, 37 |
| abstract_inverted_index.in | 9, 72 |
| abstract_inverted_index.on | 39 |
| abstract_inverted_index.to | 23, 64, 77 |
| abstract_inverted_index.DRL | 66 |
| abstract_inverted_index.Our | 16, 50 |
| abstract_inverted_index.and | 27, 60 |
| abstract_inverted_index.are | 58 |
| abstract_inverted_index.for | 5, 32, 47 |
| abstract_inverted_index.the | 41, 44, 54, 69 |
| abstract_inverted_index.Deep | 29 |
| abstract_inverted_index.HVAC | 7 |
| abstract_inverted_index.deep | 12 |
| abstract_inverted_index.show | 52 |
| abstract_inverted_index.that | 53 |
| abstract_inverted_index.well | 62 |
| abstract_inverted_index.with | 80 |
| abstract_inverted_index.focus | 38 |
| abstract_inverted_index.range | 46 |
| abstract_inverted_index.using | 11 |
| abstract_inverted_index.(DRL). | 15 |
| abstract_inverted_index.Policy | 31 |
| abstract_inverted_index.models | 74 |
| abstract_inverted_index.within | 43 |
| abstract_inverted_index.control | 8, 35 |
| abstract_inverted_index.desired | 45 |
| abstract_inverted_index.emulate | 24 |
| abstract_inverted_index.exhibit | 75 |
| abstract_inverted_index.minimal | 81 |
| abstract_inverted_index.perform | 61 |
| abstract_inverted_index.propose | 1 |
| abstract_inverted_index.results | 51 |
| abstract_inverted_index.Symbolic | 30 |
| abstract_inverted_index.building | 25 |
| abstract_inverted_index.comfort. | 49 |
| abstract_inverted_index.compared | 63 |
| abstract_inverted_index.dynamics | 26 |
| abstract_inverted_index.includes | 18 |
| abstract_inverted_index.learning | 3, 14 |
| abstract_inverted_index.occupant | 48 |
| abstract_inverted_index.policies | 57, 71 |
| abstract_inverted_index.standard | 65 |
| abstract_inverted_index.symbolic | 56 |
| abstract_inverted_index.buildings | 10 |
| abstract_inverted_index.framework | 4, 17 |
| abstract_inverted_index.policies. | 36 |
| abstract_inverted_index.surrogate | 21, 73 |
| abstract_inverted_index.discovered | 55, 70 |
| abstract_inverted_index.algorithms. | 67 |
| abstract_inverted_index.data-driven | 20 |
| abstract_inverted_index.discovering | 33 |
| abstract_inverted_index.environment | 22 |
| abstract_inverted_index.maintaining | 40 |
| abstract_inverted_index.performance | 82 |
| abstract_inverted_index.temperature | 42 |
| abstract_inverted_index.degradation. | 83 |
| abstract_inverted_index.environments | 79 |
| abstract_inverted_index.Additionally, | 68 |
| abstract_inverted_index.interpretable | 6, 34, 59 |
| abstract_inverted_index.physics-based | 78 |
| abstract_inverted_index.reinforcement | 13 |
| abstract_inverted_index.transferability | 76 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5101765243, https://openalex.org/A5023509025, https://openalex.org/A5104087723, https://openalex.org/A5004079705, https://openalex.org/A5103236436 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I1174212, https://openalex.org/I157485424, https://openalex.org/I173498003 |
| citation_normalized_percentile.value | 0.20566929 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |