BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:REQUEST X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:W. Europe Standard Time BEGIN:STANDARD DTSTART:16011028T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010325T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT ATTENDEE;CN="Vishal Rangras";RSVP=TRUE:mailto:vishal.rangras@stud.th-owl.de CLASS:PUBLIC CREATED:20211116T074738Z DESCRIPTION:Topic: NoRML: No-Reward Meta Learning\n \nhttps://arxiv.org/abs /1903.01063 \n \n\n \nAbstract: It is c ritical for agents to efficiently adapt to the\ndynamics of the environmen t in order to successfully operate in the real\nworld. The Reinforcement L earning (RL) agents typically need external\nreward feedback in order to a dapt to a task with changed dynamics.\nHowever\, for many real-world tasks \, the reward signal might not be\nreadily available. Even if the reward s ignal is available\, the\ndifference between various environments can be o nly observable from the\ndynamics and not through the feedback reward sign al. The presented paper\nproposes a new method that enables the self-adapt ation of learned\npolicies: No-Reward Meta Learning (NoRML). NoMRL is an e xtension of\nModel Agnostic Meta Learning (MAML) that uses observable dyna mics of the\nenvironment in place of explicit reward function in MAML's fi netune\nstep. This technique has a more expressive update step than MAML\, while\nit still follows a gradient-based approach. For a more targeted\ne xploration\, the presented approach implements an extension to MAML that\n effectively discounts the meta-policy parameters from the fine-tuned\npoli cies' parameters. The NoRML method is studied on several synthetic\ncontro l problems as well as common benchmark environments. The\nbenchmarking res ults show that NoRML outperforms the MAML when the\nenvironment dynamics c hange between tasks.\n \n-- Den nachstehenden Text weder löschen noch än dern. -- \n \nTreten Sie Ihrem Webex-Meeting zum gegebenen Zeitpunkt hier bei. \n \n \nMeeting beitreten\n \n \nWeitere Methoden zum Beitreten : \n \nÜber den Meeting-Link beitreten \nhttps://th-owl.webex.com/th-owl/ j.php?MTID=m5af20d6824549ef658a36e484f0\nc95b2\n \n \n \nMit Meetin g-Kennnummer beitreten \nMeeting-Kennnummer (Zugriffscode): 2732 210 4424\ nMeeting-Passwort: vZuH3Dp5M2J \n \nHier tippen\, um mit Mobilgerät beiz utreten (nur für Teilnehmer) \n+49-619-6781-9736\,\,27322104424##\n Germany Toll \n+49-619 -6781-9736\,\,27322104424##\n Germany Toll \n\nÜber Telefon beitreten \n+49-619-6781-9736 Germany Toll \n+49-619-6781-9736 Germany Toll \nGlobale Einwahlnummern\n \n \nMit Videosystem\, Anwendung oder Skype for Busin ess teilnehmen\nWählen Sie 27322104424@webex.com \nSie können auch 62.109.219.4 wählen und Ihre Meeting-Nummer eing eben. \n \nWenn Sie ein Gastgeber sind\, klicken Sie hier\n \, um Ga stgeberinformationen anzuzeigen.\n \n \nBrauchen Sie Hilfe? Gehen Sie zu h ttps://help.webex.com\n \n \n \n \n DTEND;TZID="W. Europe Standard Time":20211214T173000 DTSTAMP:20211116T074720Z DTSTART;TZID="W. Europe Standard Time":20211214T160000 LAST-MODIFIED:20211116T074738Z LOCATION:https://th-owl.webex.com/th-owl/j.php?MTID=m5af20d6824549ef658a36e 484f0c95b2 ORGANIZER;CN="Markus Lange-Hegermann":mailto:markus.lange-hegermann@th-owl. de PRIORITY:5 SEQUENCE:1 SUMMARY;LANGUAGE=en-us:MLRG: NoRML: No-Reward Meta Learning TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E0080000000080C95A3A31DAD701000000000000000 0100000004CBAEB07CD65B34D845D85D371A9EFD2 X-ALT-DESC;FMTTYPE=text/html:

Topic: \;NoRML: No-Reward Meta Learning

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https://arxiv.org/abs/1903.01063< /span>< /o:p>

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Abstract: It is critical fo r agents to efficiently \;adapt to the dynamics of the environment&nbs p\;in order to successfully operate in the real world. The \;Reinforce ment Learning (RL) agents typically \;need external reward feedback in order to adapt to \;a task with changed dynamics. However\, \;for  \;many real-world tasks\, the reward signal might not be readily avai lable. Even if the reward signal is available\, \;the difference betwe en various environments can be only observable from the dynamics and not t hrough the feedback reward signal. The presented \;paper proposes a ne w \;method that enables \;the \;self-adaptation of learned pol icies: No-Reward Meta Learning (NoRML). NoMRL is an extension of Model Agn ostic Meta Learning (MAML) that uses observable dynamics of the environmen t in place of explicit reward function in MAML's finetune step. This techn ique has a more expressive update step than MAML\, while it still follows& nbsp\;a gradient-based approach. \;For a more targeted exploration\, t he presented \;approach implements an extension to MAML that effective ly discounts the meta-policy parameters from the fine-tuned policies' para meters. The NoRML method is studied on several synthetic control problems as well as common benchmark environments. The benchmarking \;results s how that \;NoRML outperforms the MAML when the environment \;dynam ics change between tasks.

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< /tr>

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