Kyrill Schmid

Effect of energetic ions on edge-localized modes in tokamak plasmas

J. Domínguez-Palacios, S. Futatani, M. García-Muñoz, A. Jansen van Vuuren et autres

Abstract The most efficient and promising operational regime for the International Thermonuclear Experimental Reactor tokamak is the high-confinement mode. In this regime, however, periodic relaxations of the plasma edge can occur. These edge-localized modes pose a threat to the integrity of the …

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SEGym: Optimizing Large Language Model Assisted Software Engineering Agents with Reinforcement Learning

Gerhard Stenzel, Kyrill Schmid, Michael Kölle, Philipp J. Altmann et autres

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Learning to Participate through Trading of Reward Shares

Michael Kölle, Tim Matheis, Philipp J. Altmann, Kyrill Schmid

Enabling autonomous agents to act cooperatively is an important step to integrate artificial intelligence in our daily lives. While some methods seek to stimulate cooperation by letting agents give rewards to others, in this paper we propose a method inspired by the …

Learning to Participate Through Trading of Reward Shares

Michael Kölle, Tim Matheis, Philipp J. Altmann, Kyrill Schmid

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Decentralized scheduling through an adaptive, trading-based multi-agent system

Michael Kölle, Lennart Rietdorf, Kyrill Schmid

In multi-agent reinforcement learning systems, the actions of one agent can have a negative impact on the rewards of other agents. One way to combat this problem is to let agents trade their rewards amongst each other. Motivated by this, this work …

On Learning Stable Cooperation in the Iterated Prisoner's Dilemma with Paid Incentives

Xiyue Sun, Fabian R. Pieroth, Kyrill Schmid, Martin Wirsing et autres

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Solving Large Steiner Tree Problems in Graphs for Cost-efficient Fiber-To-The-Home Network Expansion

Tobias Müller, Kyrill Schmid, Daniëlle Schuman, Thomas Gabor et autres

The expansion of Fiber-To-The-Home (FTTH) networks creates high costs due to expensive excavation procedures. Optimizing the planning process and minimizing the cost of the earth excavation work therefore lead to large savings. Mathematically, the FTTH network problem can be described as a …

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Towards Multi-agent Reinforcement Learning using Quantum Boltzmann Machines

Tobias Müller, Christoph Roch, Kyrill Schmid, Philipp J. Altmann

Reinforcement learning has driven impressive advances in machine learning. Simultaneously, quantum-enhanced machine learning algorithms using quantum annealing underlie heavy developments. Recently, a multi-agent reinforcement learning (MARL) architecture combining both paradigms has been proposed. This novel algorithm, which utilizes Quantum Boltzmann Machines (QBMs) …

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Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines

Tobias Müller, Christoph Roch, Kyrill Schmid, Philipp J. Altmann

Reinforcement learning has driven impressive advances in machine learning. Simultaneously, quantum-enhanced machine learning algorithms using quantum annealing underlie heavy developments. Recently, a multi-agent reinforcement learning (MARL) architecture combining both paradigms has been proposed. This novel algorithm, which utilizes Quantum Boltzmann Machines (QBMs) …

Solving Large Steiner Tree Problems in Graphs for Cost-Efficient Fiber-To-The-Home Network Expansion

Tobias Müller, Kyrill Schmid, Daniëlle Schuman, Thomas Gabor et autres

The expansion of Fiber-To-The-Home (FTTH) networks creates high costs due to expensive excavation procedures. Optimizing the planning process and minimizing the cost of the earth excavation work therefore lead to large savings. Mathematically, the FTTH network problem can be described as a …

Stochastic Market Games

Kyrill Schmid, Lenz Belzner, Robert Müller, Johannes Tochtermann et autres

Some of the most relevant future applications of multi-agent systems like autonomous driving or factories as a service display mixed-motive scenarios, where agents might have conflicting goals. In these settings agents are likely to learn undesirable outcomes in terms of cooperation under …

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Distributed Emergent Agreements with Deep Reinforcement Learning

Kyrill Schmid, Robert Müller, Lenz Belzner, Johannes Tochtermann et autres

Building autonomous agents that are capable to cooperate with other machines is an essential step towards large scale application of AI systems. Especially systems comprised of multiple self-interested agents with general sum returns can profit from cooperative behavior as cooperation can help …

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