Dubawa
Q-Learning shine ƙarfafawa koyo algorithm wanda ke koyar da wakili wanda ayyuka suka fi dacewa ta hanyar koyon ƙimar kowane motsi ta hanyar gwaji da kuskure. Yana da mahimmanci saboda yana iya samun kyakkyawan hali ba tare da an gaya masa ƙa'idodin muhallinsa ba.
Q-Learning tubalin ginin fasaha ne wanda ke shafar ingancin samfuri, farashin kayayyakin more rayuwa, jinkiri, da aminci a sikeli.
Zurfafa nutsewa
Q-Learning yana koyon aikin da ake kira Q(s, a): lada na dogon lokaci da ake tsammanin ɗaukar mataki 'a' a cikin jaha's' sannan kuma yayi aiki da kyau bayan haka. Wakilin ya fara sanin komai, yana gwada ayyuka, kuma yana lura da lada. Bayan kowane mataki yana ƙididdige ƙimar Q-darajar sa zuwa ga ladan da aka karɓa tare da mafi kyawun ƙima mai rangwame na gaba da yake tsammani daga jiha ta gaba. Mahimmanci, ba shi da 'kayan siyasa' da 'kyakkyawan tsari': yana iya koyan mafi kyawun manufa yayin bincike ba da gangan ba, kuma baya buƙatar samfurin yadda duniya ke canzawa. Idan aka ba da isasshen bincike na kowane nau'i-nau'i na jihohi, ƙimar Q-ƙimar suna iya haɗuwa zuwa mafi kyawun dabi'u, kuma mafi kyawun aiki a kowace jiha shine kawai wanda yake da mafi girman Q.
Fahimtar Fasaha
Babban shine sabunta Bellman: Q(s,a) <- Q(s,a) + alpha[r + gamma*max_a'Q(s',a') - Q(s,a)]. Alpha shine ƙimar koyo, gamma rangwamen ma'auni mai nauyin lada na gaba, kuma madaidaicin lokaci shine kuskuren bambancin ɗan lokaci. Babban 'max' a kan ayyuka na gaba shine abin da ya sa ya zama mara amfani kuma yana ba shi damar koyon ingantacciyar manufa ko da yayin bincike. Ana gudanar da bincike yawanci tare da zaɓin aikin epsilon-m.
Jagoran Q-Learning
Q-Learning shine ƙarfafawa koyo algorithm wanda ke koyar da wakili wanda ayyuka suka fi dacewa ta hanyar koyon ƙimar kowane motsi ta hanyar gwaji da kuskure. Yana da mahimmanci saboda yana iya samun kyakkyawan hali ba tare da an gaya masa ƙa'idodin muhallinsa ba. Q-Learning tubalin ginin fasaha ne wanda ke shafar ingancin samfuri, farashin kayayyakin more rayuwa, jinkiri, da aminci a sikeli. Don gina zurfin fahimta, bi da Q-Learning a matsayin samfurin aiki, ba fasali ɗaya ba: ayyana sakamakon da ake so, bayyana zato, da raba abin da tsarin zai iya yi da dogaro daga abin da har yanzu ke buƙatar yanke hukunci na ƙwararru.
A aikace, ƙungiyoyi masu ƙarfi masu amfani da Q-Learning suna haɓaka gine-gine, bayanai, da zaɓin abubuwan more rayuwa a kan dogaro da farashi. Suna rubuta ƙayyadaddun ƙa'idodin nasara, gwaji akan bayanan gaskiya da gudanawar aiki, da jujjuyawar bisa ga tsarin gazawar da aka lura maimakon cin nasara na lokaci ɗaya. Wannan shine inda fahimtar ka'idar ta juya zuwa iyawa mai dorewa a cikin samfura, manufofi, da ayyuka.
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru. A lokaci guda, Haɓaka ma'auni ɗaya na iya ɓoye manyan raunin tsarin. Hanyar da ta fi dacewa ita ce haɗa saurin gwaji tare da horon gudanarwa: gudanar da matukin jirgi, kama shaida, buga rajistan ayyukan yanke shawara, da ci gaba da sabunta abubuwan tsaro kamar yadda halayen ƙira, tsammanin mai amfani, da buƙatun tsari ke tasowa.
Dabarun Tasiri
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru.
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru. A cikin ƙawance masu inganci, ana fassara wannan zuwa ƙa'idodin aiki waɗanda za a iya aunawa, iyakokin ikon mallaka, da kuma bita-da-kullin bita don ƙungiyoyi su iya haɓaka kwarin gwiwa a maimakon ɓata shakku.
Ilimin fasaha yana taimaka wa ƙungiyoyi su zaɓi tari mai kyau, ba kawai sabon abu ba.
Ilimin fasaha yana taimaka wa ƙungiyoyi su zaɓi tari mai kyau, ba kawai sabon abu ba. A cikin ƙawance masu inganci, ana fassara wannan zuwa ƙa'idodin aiki waɗanda za a iya aunawa, iyakokin ikon mallaka, da kuma bita-da-kullin bita don ƙungiyoyi su iya haɓaka kwarin gwiwa a maimakon ɓata shakku.
Zaɓuɓɓukan injiniya mafi kyau suna rage abin dogaro a cikin samarwa.
Zaɓuɓɓukan injiniya mafi kyau suna rage abin dogaro a cikin samarwa. A cikin ƙawance masu inganci, ana fassara wannan zuwa ƙa'idodin aiki waɗanda za a iya aunawa, iyakokin ikon mallaka, da kuma bita-da-kullin bita don ƙungiyoyi su iya haɓaka kwarin gwiwa a maimakon ɓata shakku.
Aiwatar da Gaskiyar Duniya
Wakilan wasan Atari (DeepMind's DQN) koyan kunna Breakout da Pong kai tsaye daga pixels allo
Haɓaka lokacin hasken zirga-zirga a tsaka-tsaki don rage jimlar lokacin jiran abin hawa
Kewayawa Robot ta hanyar grid ko maze inda mutum-mutumi ya koyi mafi guntun hanya mafi girman lada
Madaidaicin farashin farashi da yanke shawarar ƙira inda wakili ya koyi ayyukan da ke haɓaka riba mai tsayi
Hanyoyin Aiwatarwa
Q-Koyo a aikace
Wakilan wasan Atari (DeepMind's DQN) koyan kunna Breakout da Pong kai tsaye daga pixels allo.
Wakilan wasan wasan Atari (DeepMind's DQN) koyan yin wasa Breakout da Pong kai tsaye daga pixels na allo Ƙungiyoyi yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓakar ɗan adam don ƙararraki, da kuma bin diddigin nasarorin samarwa da ƙimar kuskure akan lokaci.
Q-Koyo a aikace
Haɓaka lokacin hasken zirga-zirga a tsaka-tsaki don rage jimlar lokacin jiran abin hawa.
Haɓaka lokacin hasken zirga-zirgar ababen hawa a tsaka-tsaki don rage jimlar lokacin jiran abin hawa Ƙungiyoyi yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓakar ɗan adam don ƙararraki, da bin duk nasarorin samarwa da ƙimar kuskure akan lokaci.
Q-Koyo a aikace
Kewayawa Robot ta hanyar grid ko maze inda mutum-mutumi ya koyi mafi guntun hanya mafi girman lada.
Kewayawa Robot ta hanyar grid ko maze inda robot ke koyon mafi guntun lada-mallakar hanya Ƙungiyoyi yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓaka ɗan adam don ƙararraki, da bin diddigin nasarorin samarwa da tsadar kuɗi na tsawon lokaci.
Q-Koyo a aikace
Madaidaicin farashin farashi da yanke shawarar ƙira inda wakili ya koyi ayyukan da ke haɓaka riba mai tsayi.
Matsakaicin farashin farashi da yanke shawara na ƙira inda wakili ya koya waɗanne ayyuka ke haɓaka riba mai tsayi Ƙungiyoyi yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ƙofofin inganci a gaba, kiyaye hanyar haɓakar ɗan adam don ƙararraki, da bin duk nasarorin samarwa da ƙimar kuskure akan lokaci.
Hatsari & Tsare-tsare
Haɓaka ma'auni ɗaya na iya ɓoye manyan raunin tsarin.
Sau da yawa ana raina kayan more rayuwa da kuma kuɗin kulawa.
Tsaro da gibin lura na iya girma yayin da tsarin ke ƙara haɓaka.
Taswirar Hanya
Ƙayyade latency, inganci, da maƙasudin farashi kafin aiwatarwa.
Ƙayyade latency, inganci, da maƙasudin farashi kafin aiwatarwa. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.
Alamar ma'auni a ƙarƙashin ainihin kaya da yanayin bayanai.
Alamar ma'auni a ƙarƙashin ainihin kaya da yanayin bayanai. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.
Kula da kayan aiki don kurakurai, ɗigo, da tasirin mai amfani.
Kula da kayan aiki don kurakurai, ɗigo, da tasirin mai amfani. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.
Shirya bijirowa da hanyoyin mayar da martani kafin sikeli.
Shirya bijirowa da hanyoyin mayar da martani kafin sikeli. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.