On-The-Fly Compute Centers II: Execution of Composed Services in Configurable Compute Centers
H. Karl, M. Maack, F. Meyer auf der Heide, S. Pukrop, A. Redder, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 183–202.

mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks
S.B. Schneider, S. Werner, R. Khalili, A. Hecker, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.

Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law
A. Redder, A. Ramaswamy, H. Karl, in: Proceedings of the 58th Allerton Conference on Communication, Control, and Computing, 2022.

Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication
A. Redder, A. Ramaswamy, H. Karl, in: Proceedings of the 14th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, 2022.

Distributed gradient-based optimization in the presence of dependent  aperiodic communication
A. Redder, A. Ramaswamy, H. Karl, ArXiv:2201.11343 (2022).

Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms
A. Redder, A. Ramaswamy, H. Karl, ArXiv:2201.00570 (2022).

Practical Network Conditions for the Convergence of Distributed Optimization
A. Redder, A. Ramaswamy, H. Karl, IFAC-PapersOnLine 55 (2022) 133–138.

Use What You Know: Network and Service Coordination Beyond Certainty
S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.

Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding
H. Afifi, F.J. Sauer, H. Karl, in: 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21), Hyderabad, India, 2021.

Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities
H. Afifi, M. Guenther, A. Brendel, H. Karl, W. Kellermann, in: 14. ITG Conference on Speech Communication (ITG 2021), 2021.

Network-Aware Optimal Microphone Channel Selection in Wireless Acoustic Sensor Networks
M. Gunther, H. Afifi, A. Brendel, H. Karl, W. Kellermann, in: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.

Learning Flow Scheduling
A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications &; Networking Conference (CCNC), IEEE Computer Society, n.d.

Learning Coflow Admissions
A. Hasnain, H. Karl, in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, n.d.

Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks
H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium), Montreal, Canada, 2021.

A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks
H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications \'26; Networking Conference (CCNC) (CCNC 2021), 2021.

Distributed Online Service Coordination Using Deep Reinforcement Learning
S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE, 2021.

Divide and Conquer: Hierarchical Network and Service Coordination
S.B. Schneider, M. Jürgens, H. Karl, in: IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP/IEEE, 2021.

Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning
S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, A. Hecker, Transactions on Network and Service Management (2021).

DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning
S.B. Schneider, H. Karl, R. Khalili, A. Hecker, DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning, 2021.

Every Node for Itself: Fully Distributed Service Coordination
S.B. Schneider, L.D. Klenner, H. Karl, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.