The Altitude Control (left) and Training Curve (right) obtained on SAC agent.
<p>The Altitude Control (left) and Training Curve (right) obtained on SAC agent.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
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| _version_ | 1852015875825074176 |
|---|---|
| author | Hasan Raza Khanzada (22404835) |
| author2 | Adnan Maqsood (22404838) Abdul Basit (174463) |
| author2_role | author author |
| author_facet | Hasan Raza Khanzada (22404835) Adnan Maqsood (22404838) Abdul Basit (174463) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hasan Raza Khanzada (22404835) Adnan Maqsood (22404838) Abdul Basit (174463) |
| dc.date.none.fl_str_mv | 2025-10-09T20:08:01Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0334219.g019 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_Altitude_Control_left_and_Training_Curve_right_obtained_on_SAC_agent_/30324082 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Biotechnology Ecology Inorganic Chemistry Science Policy Biological Sciences not elsewhere classified critical gap persists classical pid controllers 3 %, offering varying flight conditions suitable rl algorithm space rl algorithms rl algorithms outperformed provide valuable insight comparative analysis reveals uav flight controls evaluated rl algorithms comparative analysis flight control rl agents rl ). analysis aims wing uavs wind gusts uncertain environments uavs ). state error soft actor rigorous evaluation results demonstrate relative strengths reinforcement learning recent studies paper aims major shift leading continuous environmental disturbances deliver robust control pitch best trade 400 episodes |
| dc.title.none.fl_str_mv | The Altitude Control (left) and Training Curve (right) obtained on SAC agent. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The Altitude Control (left) and Training Curve (right) obtained on SAC agent.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_062072c339fd174cd1da68cfadd34968 |
| identifier_str_mv | 10.1371/journal.pone.0334219.g019 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30324082 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The Altitude Control (left) and Training Curve (right) obtained on SAC agent.Hasan Raza Khanzada (22404835)Adnan Maqsood (22404838)Abdul Basit (174463)Cell BiologyBiotechnologyEcologyInorganic ChemistryScience PolicyBiological Sciences not elsewhere classifiedcritical gap persistsclassical pid controllers3 %, offeringvarying flight conditionssuitable rl algorithmspace rl algorithmsrl algorithms outperformedprovide valuable insightcomparative analysis revealsuav flight controlsevaluated rl algorithmscomparative analysisflight controlrl agentsrl ).analysis aimswing uavswind gustsuncertain environmentsuavs ).state errorsoft actorrigorous evaluationresults demonstraterelative strengthsreinforcement learningrecent studiespaper aimsmajor shiftleading continuousenvironmental disturbancesdeliver robustcontrol pitchbest trade400 episodes<p>The Altitude Control (left) and Training Curve (right) obtained on SAC agent.</p>2025-10-09T20:08:01ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0334219.g019https://figshare.com/articles/figure/The_Altitude_Control_left_and_Training_Curve_right_obtained_on_SAC_agent_/30324082CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303240822025-10-09T20:08:01Z |
| spellingShingle | The Altitude Control (left) and Training Curve (right) obtained on SAC agent. Hasan Raza Khanzada (22404835) Cell Biology Biotechnology Ecology Inorganic Chemistry Science Policy Biological Sciences not elsewhere classified critical gap persists classical pid controllers 3 %, offering varying flight conditions suitable rl algorithm space rl algorithms rl algorithms outperformed provide valuable insight comparative analysis reveals uav flight controls evaluated rl algorithms comparative analysis flight control rl agents rl ). analysis aims wing uavs wind gusts uncertain environments uavs ). state error soft actor rigorous evaluation results demonstrate relative strengths reinforcement learning recent studies paper aims major shift leading continuous environmental disturbances deliver robust control pitch best trade 400 episodes |
| status_str | publishedVersion |
| title | The Altitude Control (left) and Training Curve (right) obtained on SAC agent. |
| title_full | The Altitude Control (left) and Training Curve (right) obtained on SAC agent. |
| title_fullStr | The Altitude Control (left) and Training Curve (right) obtained on SAC agent. |
| title_full_unstemmed | The Altitude Control (left) and Training Curve (right) obtained on SAC agent. |
| title_short | The Altitude Control (left) and Training Curve (right) obtained on SAC agent. |
| title_sort | The Altitude Control (left) and Training Curve (right) obtained on SAC agent. |
| topic | Cell Biology Biotechnology Ecology Inorganic Chemistry Science Policy Biological Sciences not elsewhere classified critical gap persists classical pid controllers 3 %, offering varying flight conditions suitable rl algorithm space rl algorithms rl algorithms outperformed provide valuable insight comparative analysis reveals uav flight controls evaluated rl algorithms comparative analysis flight control rl agents rl ). analysis aims wing uavs wind gusts uncertain environments uavs ). state error soft actor rigorous evaluation results demonstrate relative strengths reinforcement learning recent studies paper aims major shift leading continuous environmental disturbances deliver robust control pitch best trade 400 episodes |