H. Karl, M. Maack, F. Meyer auf der Heide, S. Pukrop, and A. Redder, “On-The-Fly Compute Centers II: Execution of Composed Services in Configurable Compute Centers,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 183–202.

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

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

A. Redder, A. Ramaswamy, and H. Karl, “Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication,”10.5220/0010845400003116__2022, doi:10.5220/0010845400003116 .

A. Redder, A. Ramaswamy, and H. Karl, “Distributed gradient-based optimization in the presence of dependent  aperiodic communication,” arXiv:2201.11343. 2022.

A. Redder, A. Ramaswamy, and H. Karl, “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms,” arXiv:2201.00570. 2022.

A. Redder, A. Ramaswamy, and H. Karl, “Practical Network Conditions for the Convergence of Distributed Optimization,” IFAC-PapersOnLine, vol. 55, no. 13, pp. 133–138, 2022.

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

H. Afifi, F. J. Sauer, and H. Karl, “Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding,” 2021.

H. Afifi, M. Guenther, A. Brendel, H. Karl, and W. Kellermann, “Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities,” 2021.

M. Gunther, H. Afifi, A. Brendel, H. Karl, and W. Kellermann, “Network-Aware Optimal Microphone Channel Selection in Wireless Acoustic Sensor Networks,”10.1109/icassp39728.2021.9414528__2021, doi:10.1109/icassp39728.2021.9414528 .

A. Hasnain and H. Karl, “Learning Flow Scheduling,” in 2021 IEEE 18th Annual Consumer Communications &; Networking Conference (CCNC), Las Vegas, USA.

A. Hasnain and H. Karl, “Learning Coflow Admissions,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver BC Canada.

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

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

S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination Using Deep Reinforcement Learning,” in IEEE International Conference on Distributed Computing Systems (ICDCS), Washington, DC, USA, 2021.

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

S. B. Schneider et al., “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning,” Transactions on Network and Service Management, 2021.

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

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