BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Safe Reinforcement Learning: Building Agents You Can Trust 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:2025-05-05-davide-corsi@cml.ics.uci.edu DTSTAMP:20250505T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20250505T160000 DTEND;TZID=America/Los_Angeles:20250505T170000 SUMMARY:[CML Seminar] Davide Corsi: Safe Reinforcement Learning: Building A gents You Can Trust LOCATION:Interdisciplinary Science & Engineering Building 1010 DESCRIPTION:Davide Corsi\, Postdoctoral Researcher\, Department of Computer Science\, University of California\, Irvine\n\nTitle: Safe Reinforcement Learning: Building Agents You Can Trust\n\nAbstract: Reinforcement learnin g is increasingly used to train robots for tasks where safety is critical\ , such as autonomous surgery and navigation. However\, when combined with deep neural networks\, these systems can become unpredictable and difficul t to trust in contexts where even a single error is often unacceptable. Th is talk explores two complementary paths toward safer reinforcement learni ng: making agents more reliable through constrained training\, and adding formal guarantees through techniques such as verification and shielding. I n the second part of the talk\, we will look at the growing role of world modeling in robotics and how this\, together with the rise of large founda tion models\, opens up new challenges for ensuring safety in complex\, rea l-world environments.\n\nhttps://cml.ics.uci.edu/seminars/2025-05-05-david e-corsi X-ALT-DESC;FMTTYPE=text/html:Davide Corsi\, Postdoctoral Researcher\, Department of Computer Science\, University of California\, Irvine

Title: Safe Reinforcement Learning: Building Agents Y ou Can Trust

Abstract: Reinforcement learning is increasingl y used to train robots for tasks where safety is critical\, such as autono mous surgery and navigation. However\, when combined with deep neural netw orks\, these systems can become unpredictable and difficult to trust in co ntexts where even a single error is often unacceptable. This talk explores two complementary paths toward safer reinforcement learning: making agent s more reliable through constrained training\, and adding formal guarantee s through techniques such as verification and shielding. In the second par t of the talk\, we will look at the growing role of world modeling in robo tics and how this\, together with the rise of large foundation models\, op ens up new challenges for ensuring safety in complex\, real-world environm ents.

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