BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//TECoSA - ECPv6.17.3.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:TECoSA
X-ORIGINAL-URL:https://www.tecosa.center.kth.se
X-WR-CALDESC:Events for TECoSA
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20230929T120000
DTEND;TZID=UTC:20230929T130000
DTSTAMP:20230821T114938Z
CREATED:20230821T114938Z
LAST-MODIFIED:20230821T114938Z
UID:6797-1695988800-1695992400@www.tecosa.center.kth.se
SUMMARY:TECoSA Research Seminar: Explainable Reinforcement Learning for Telecom
DESCRIPTION:Speakers:  Industrial PhD students Ahmad Terra (TECoSA\, Ericsson) and Franco Ruggeri (Ericsson)\nLocation: C435\, Brinellvägen 85 (plan 4)\nZoom link and sign-up link circulated to members\nPlease email vickid@kth.se if you have any questions. \nABSTRACT: Explainable Reinforcement Learning (XRL) primarily to provide explainability for black-box models to enable trust. XRL includes multiple methods applied to different elements of RL agent (state\, rewards\, policy explanations\, etc.). This seminar will present multiple existing state-of-the-art XRL methods\, their application to different elements especially to the remote electrical antenna tilt (RET) use case. Additionally\, the seminar includes a new method “Both Ends Explanations for RL (BEERL)” that goes one-step further to connect and compare input and output explanations\, identify and mitigate bias\, and generates two different levels of explanation which in turn allows RL agent re-configurations when unwanted behaviours are observed.
URL:https://www.tecosa.center.kth.se/event/tecosa-research-seminar-explainable-reinforcement-learning-for-telecom/
CATEGORIES:Talks
END:VEVENT
END:VCALENDAR