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UID:924@lincs.fr
DTSTART;TZID=Europe/Paris:20251015T140000
DTEND;TZID=Europe/Paris:20251015T150000
DTSTAMP:20251020T102022Z
URL:https://www.lincs.fr/events/multi-agent-reinforcement-learning-for-url
 lc-modern-random-accesstbd-17/
SUMMARY:Multi-Agent Reinforcement Learning for URLLC Modern Random Access
DESCRIPTION:Future wireless networks are expected to support a wide range
 of use cases with diverse requirements. Among these\, Ultra-Reliable
 Low-Latency Communications (URLLC) stand out as a key enabler for
 transformative applications across industries such as intelligent
 transportation\, smart grids\, and industrial automation.\n\nThese
 applications demand extremely high transmission reliability coupled with
 very low latency\, posing stringent quality of service (QoS) challenges.
 Since the emergence of 5G\, numerous enhancements have been introduced to
 meet URLLC demands.\n\nHowever\, ensuring adequate QoS still requires the
 design of new medium access control (MAC) protocols and advanced scheduling
 policies. In this seminar\, we present recent research addressing the
 scheduling of URLLC traffic on the uplink using Modern Random Access (MRA)
 and Multi-Agent Reinforcement Learning (MARL).\n\nWe first formulate the
 URLLC MRA (uMRA) problem as a Decentralized Partially Observable Markov
 Decision Process (Dec-POMDP). We then introduce uMRA-HAPPO\, a MARL-based
 solution employing the Heterogeneous Agents Proximal Policy Optimization
 (HAPPO) algorithm.\n\nFinally\, we evaluate our approach in scenarios
 inspired by 3GPP use cases\, demonstrating that uMRA-HAPPO achieves higher
 reliability than traditional deterministic and random scheduling
 strategies\, while satisfying strict latency constraints.
CATEGORIES:Seminars,Youtube
LOCATION:Amphi 7\, 19 Place Marguerite Perey\, Palaiseau\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 Place Marguerite Perey\,
 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Amphi 7:geo:0,0
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TZID:Europe/Paris
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DTSTART:20250330T030000
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