BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:CrystalBox: Future-Based Explanations for Deep RL Network Cont rollers X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2023-06-05-sangeetha-abdu-jyothi@cml.ics.uci.edu DTSTAMP:20230605T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20230605T130000 DTEND;TZID=America/Los_Angeles:20230605T140000 SUMMARY:[CML Seminar] Sangeetha Abdu Jyothi: CrystalBox: Future-Based Expla nations for Deep RL Network Controllers LOCATION:Donald Bren Hall 4011 DESCRIPTION:Sangeetha Abdu Jyothi\, Assistant Professor of Computer Science \, University of California\, Irvine\n\nTitle: CrystalBox: Future-Based Ex planations for Deep RL Network Controllers\n\nAbstract: Lack of explainabi lity is a key factor limiting the practical adoption of high-performant De ep Reinforcement Learning (DRL) controllers in systems environments. Expla inable RL for networking hitherto used salient input features to interpret a controller's behavior. However\, these feature-based solutions do not c ompletely explain the controller's decision-making process. In this talk\, I will present CrystalBox\, a framework that explains a controller's beha vior in terms of the future impact on key network performance metrics. Cry stalBox employs a novel learning-based approach to generate succinct and e xpressive explanations\, using reward components of the DRL controller as the basis. I will present three practical use cases: cross-state explainab ility\, guided reward design\, and network observability.\n\nhttps://cml.i cs.uci.edu/seminars/2023-06-05-sangeetha-abdu-jyothi X-ALT-DESC;FMTTYPE=text/html:Sangeetha Abdu Jyothi\, Ass istant Professor of Computer Science\, University of California\, Irvine
Title: CrystalBox: Future-Based Explanations for Deep RL Netw ork Controllers

Abstract: Lack of explainability is a key fa ctor limiting the practical adoption of high-performant Deep Reinforcement Learning (DRL) controllers in systems environments. Explainable RL for ne tworking hitherto used salient input features to interpret a controller's behavior. However\, these feature-based solutions do not completely explai n the controller's decision-making process. In this talk\, I will present CrystalBox\, a framework that explains a controller's behavior in terms of the future impact on key network performance metrics. CrystalBox employs a novel learning-based approach to generate succinct and expressive explan ations\, using reward components of the DRL controller as the basis. I wil l present three practical use cases: cross-state explainability\, guided r eward design\, and network observability.

https://cml.ics.uci.edu /seminars/2023-06-05-sangeetha-abdu-jyothi URL:https://cml.ics.uci.edu/seminars/2023-06-05-sangeetha-abdu-jyothi END:VEVENT END:VCALENDAR