activity
20232026
collaborators

7 papers

quant-ph2026

SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networks

Tobias Meuser, Jannis Weil, Aninda Lahiri +1

Quantum networks are expected to become a key enabler for interconnecting quantum devices. In contrast to classical communication networks, however, information transfer in quantum…

cs.NI2026

Analyzing Symbolic Properties for DRL Agents in Systems and Networking

Mohammad Zangooei, Jannis Weil, Amr Rizk +2

Deep reinforcement learning (DRL) has shown remarkable performance on complex control problems in systems and networking, including adaptive video streaming, wireless resource mana…

quant-ph2025

RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks

Tobias Meuser, Jannis Weil, Aninda Lahiri +1

Quantum networks are becoming increasingly important because of advancements in quantum computing and quantum sensing, such as recent developments in distributed quantum computing…

cs.MA2024

FairStream: Fair Multimedia Streaming Benchmark for Reinforcement Learning Agents

Jannis Weil, Jonas Ringsdorf, Julian Barthel +2

Multimedia streaming accounts for the majority of traffic in today's internet. Mechanisms like adaptive bitrate streaming control the bitrate of a stream based on the estimated ban…

cs.MA2024

Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing

Jannis Weil, Zhenghua Bao, Osama Abboud +1

Graph-based environments pose unique challenges to multi-agent reinforcement learning. In decentralized approaches, agents operate within a given graph and make decisions based on…

cs.MA2023

Learning to Cooperate and Communicate Over Imperfect Channels

Jannis Weil, Gizem Ekinci, Heinz Koeppl +1

Information exchange in multi-agent systems improves the cooperation among agents, especially in partially observable settings. In the real world, communication is often carried ou…

cs.AI2023

Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman

Jannis Weil, Johannes Czech, Tobias Meuser +1

In combination with Reinforcement Learning, Monte-Carlo Tree Search has shown to outperform human grandmasters in games such as Chess, Shogi and Go with little to no prior domain k…