The agent-environment interaction in reinforcement learning [3].
<p>The agent-environment interaction in reinforcement learning [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0312559#pone.0312559.ref003" target="_blank">3</a>].</p>
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2025
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| _version_ | 1852023531911512064 |
|---|---|
| author | Raed Alharthi (18340157) |
| author2 | Iram Noreen (12334120) Amna Khan (735548) Turki Aljrees (16715369) Zoraiz Riaz (20571028) Nisreen Innab (20389920) |
| author2_role | author author author author author |
| author_facet | Raed Alharthi (18340157) Iram Noreen (12334120) Amna Khan (735548) Turki Aljrees (16715369) Zoraiz Riaz (20571028) Nisreen Innab (20389920) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raed Alharthi (18340157) Iram Noreen (12334120) Amna Khan (735548) Turki Aljrees (16715369) Zoraiz Riaz (20571028) Nisreen Innab (20389920) |
| dc.date.none.fl_str_mv | 2025-01-16T18:45:18Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0312559.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_agent-environment_interaction_in_reinforcement_learning_3_/28223215 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Sociology Science Policy Mental Health Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified robotic systems due could help deal artificial intelligence field art comparison shows motion planning addresses based motion planning problem becomes worse based reinforcement algorithm reward system ’ task learning due deep learning integration th </ sup proposed approach ’ div >< p cluttered passage environment causes late convergence complex environment cluttered narrow passage environment proposed approach cluttered environment complex environment reinforcement learning navigation problem reward principles reward policies path planning timely responses research presents remarkable aspect processing requirements novel q many applications less responsive less efficient issues using existing algorithms computationally expensive collision avoidance agent converged |
| dc.title.none.fl_str_mv | The agent-environment interaction in reinforcement learning [3]. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The agent-environment interaction in reinforcement learning [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0312559#pone.0312559.ref003" target="_blank">3</a>].</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_cc2fecae0ea4bb3db7ada753100a71d7 |
| identifier_str_mv | 10.1371/journal.pone.0312559.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28223215 |
| 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 agent-environment interaction in reinforcement learning [3].Raed Alharthi (18340157)Iram Noreen (12334120)Amna Khan (735548)Turki Aljrees (16715369)Zoraiz Riaz (20571028)Nisreen Innab (20389920)SociologyScience PolicyMental HealthSpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedrobotic systems duecould help dealartificial intelligence fieldart comparison showsmotion planning addressesbased motion planningproblem becomes worsebased reinforcement algorithmreward system ’task learning duedeep learning integrationth </ supproposed approach ’div >< pcluttered passage environmentcauses late convergencecomplex environment clutterednarrow passage environmentproposed approachcluttered environmentcomplex environmentreinforcement learningnavigation problemreward principlesreward policiespath planningtimely responsesresearch presentsremarkable aspectprocessing requirementsnovel qmany applicationsless responsiveless efficientissues usingexisting algorithmscomputationally expensivecollision avoidanceagent converged<p>The agent-environment interaction in reinforcement learning [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0312559#pone.0312559.ref003" target="_blank">3</a>].</p>2025-01-16T18:45:18ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0312559.g001https://figshare.com/articles/figure/The_agent-environment_interaction_in_reinforcement_learning_3_/28223215CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/282232152025-01-16T18:45:18Z |
| spellingShingle | The agent-environment interaction in reinforcement learning [3]. Raed Alharthi (18340157) Sociology Science Policy Mental Health Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified robotic systems due could help deal artificial intelligence field art comparison shows motion planning addresses based motion planning problem becomes worse based reinforcement algorithm reward system ’ task learning due deep learning integration th </ sup proposed approach ’ div >< p cluttered passage environment causes late convergence complex environment cluttered narrow passage environment proposed approach cluttered environment complex environment reinforcement learning navigation problem reward principles reward policies path planning timely responses research presents remarkable aspect processing requirements novel q many applications less responsive less efficient issues using existing algorithms computationally expensive collision avoidance agent converged |
| status_str | publishedVersion |
| title | The agent-environment interaction in reinforcement learning [3]. |
| title_full | The agent-environment interaction in reinforcement learning [3]. |
| title_fullStr | The agent-environment interaction in reinforcement learning [3]. |
| title_full_unstemmed | The agent-environment interaction in reinforcement learning [3]. |
| title_short | The agent-environment interaction in reinforcement learning [3]. |
| title_sort | The agent-environment interaction in reinforcement learning [3]. |
| topic | Sociology Science Policy Mental Health Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified robotic systems due could help deal artificial intelligence field art comparison shows motion planning addresses based motion planning problem becomes worse based reinforcement algorithm reward system ’ task learning due deep learning integration th </ sup proposed approach ’ div >< p cluttered passage environment causes late convergence complex environment cluttered narrow passage environment proposed approach cluttered environment complex environment reinforcement learning navigation problem reward principles reward policies path planning timely responses research presents remarkable aspect processing requirements novel q many applications less responsive less efficient issues using existing algorithms computationally expensive collision avoidance agent converged |