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Nhungamiro yehunyanzvi
Inverse reinforcement learning (IRL) flips standard RL: panzvimbo yekupihwa mubairo nekutsvaga policy, inotarisa hunhu hwenyanzvi uye inopinza basa rakavanzika remubairo rinotsanangura.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
This matters because a recovered reward generalizes to new situations far better than directly copied actions.
Inverse yekusimbisa kudzidza inobvunza: ndechipi chinangwa chinofanira kunge chiri kutsvagirwa nenyanzvi kuzvibata nenzira yavakaita? Zvichipa zviratidziro, IRL inotora basa remubairo pasi payo iyo maitiro anotaridzika zvakanaka (kana padyo-yakanyanya), yobva yashandisa yakajairwa RL kutora mutemo. Kurudziro ndeyekuita - mubairo wakadzidzwa unobata chikonzero chekuseri kwemaitiro, saka mumiriri anogona kuita zvine hungwaru mumatunhu maratidziro asina kumbovharwa, kusiyana nekuita cloning inongotevedzera zviito. Dambudziko ndere kurwara zvakanyanya: mabasa mazhinji emubairo anotsanangura maitiro akafanana, kusanganisira mashoma. Nzira dzakakosha dzinogadzirisa kusanzwisisika uku, kusanganisira nzira dzepamusoro-dzepamusoro dzinosarudza mibairo inoita kuti nyanzvi inyatsojeka, uye yakanyanya-entropy IRL, iyo inotora mubairo wekupa mubairo unoenderana nedata.
Mota dzinozvimirira dzinoratidza zvido zvekutyaira (kupfava, miganho yekuchengetedza) kubva kune vatyairi vevanhu
Marobhoti anodzidza zvinangwa zvebasa kubva mukuratidzira kwevanhu kuti aite kuti zvive zvitsva
Kutevedzera mafambiro evanofamba netsoka kana mhuka nekudzoreredza zvibodzwa zviri kuseri kwemakwara akacherechedzwa
Reward inference yeAI kurongeka, kudzidza tsika dzevanhu kubva kune dzakaratidzwa sarudzo
IRL inowedzera kusimbisa kudzidza kwemubairo wekuenzanisa: pane vanhu mibairo yekukodha ruoko, masisitimu anoratidza izvo vanhu vanokoshesa kubva muhunhu uye mhinduro. Tarisira hukama hwakasimba nekusimbisa kudzidza kubva kumhinduro dzevanhu uye kudzidza kwaunofarira, kukwira kune yemutauro-modhi uye marobhoti marobhoti. Tsvagiridzo iri kusunda kudzoreredza mibairo kubva kuvhidhiyo yakasvibirira uye nekutarisa zvishoma, uye kune inooneka mibairo inopikisa mubairo-kubira uye kusanzwisisika matambudziko ari kunetsa nzira dzanhasi.
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Inverse reinforcement learning (IRL) flips standard RL: panzvimbo yekupihwa mubairo nekutsvaga policy, inotarisa hunhu hwenyanzvi uye inopinza basa rakavanzika remubairo rinotsanangura. Izvi zvine basa nekuti mubairo wakadzoserwa unoenderana nemamiriro matsva zvirinani pane zviito zvakakopwa zvakananga.
IRL inotora zviratidziro sekuisa uye infers iyo iri pasi pemubairo basa pasi iyo maitiro enyanzvi anooneka akanyatso.
Mabasa akawanda emubairo, kusanganisira mubairo we-zero, anogona kuita kuti maitiro akacherechedzwa ave akakwana, saka mubairo hausi wakatemerwa nekuratidzira chete.
Basa remubairo rinoisa kuti nei nyanzvi yakaita sezvayakaita, ichirega iyo yakatorwa mutemo ichiita zvine hungwaru mumatunhu matsva, nepo cloning chete makopi zviito zvinoonekwa mune data.
Max-entropy IRL inotora mibairo yepamusoro-soro inogoneka zvakanyanya, ichipa chinangwa chakasiyana chinoshivirira vasina kukwana, vane ruzha nyanzvi.
IRL inogadzira mubairo, unozopihwa kune yakajairwa RL algorithm kuverengera yakaringana mutemo pasi peicho chinangwa chakapihwa.
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