--- _id: '50066' author: - first_name: Feng full_name: Dou, Feng last_name: Dou - first_name: Lin full_name: Wang, Lin id: '102868' last_name: Wang - first_name: Shutong full_name: Chen, Shutong last_name: Chen - first_name: Fangming full_name: Liu, Fangming last_name: Liu citation: ama: 'Dou F, Wang L, Chen S, Liu F. X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics. In: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE.' apa: 'Dou, F., Wang, L., Chen, S., & Liu, F. (n.d.). X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics. Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.' bibtex: '@inproceedings{Dou_Wang_Chen_Liu, title={X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics}, booktitle={Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}, publisher={IEEE}, author={Dou, Feng and Wang, Lin and Chen, Shutong and Liu, Fangming} }' chicago: 'Dou, Feng, Lin Wang, Shutong Chen, and Fangming Liu. “X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics.” In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE, n.d.' ieee: 'F. Dou, L. Wang, S. Chen, and F. Liu, “X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics,” presented at the IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.' mla: 'Dou, Feng, et al. “X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics.” Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), IEEE.' short: 'F. Dou, L. Wang, S. Chen, F. Liu, in: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), IEEE, n.d.' conference: end_date: 2024-05-23 location: Vancouver, Canada name: IEEE International Conference on Computer Communications (INFOCOM) start_date: 2024-05-20 date_created: 2023-12-22T20:24:27Z date_updated: 2024-01-23T20:35:02Z department: - _id: '34' - _id: '7' - _id: '75' language: - iso: eng publication: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) publication_status: accepted publisher: IEEE status: public title: 'X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics' type: conference user_id: '102868' year: '2024' ... --- _id: '50065' author: - first_name: Marcel full_name: Blöcher, Marcel last_name: Blöcher - first_name: Nils full_name: Nedderhut, Nils last_name: Nedderhut - first_name: Pavel full_name: Chuprikov, Pavel last_name: Chuprikov - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Patrick full_name: Eugster, Patrick last_name: Eugster - first_name: Lin full_name: Wang, Lin id: '102868' last_name: Wang citation: ama: 'Blöcher M, Nedderhut N, Chuprikov P, Khalili R, Eugster P, Wang L. Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES. In: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE.' apa: 'Blöcher, M., Nedderhut, N., Chuprikov, P., Khalili, R., Eugster, P., & Wang, L. (n.d.). Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES. Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.' bibtex: '@inproceedings{Blöcher_Nedderhut_Chuprikov_Khalili_Eugster_Wang, title={Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES}, booktitle={Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}, publisher={IEEE}, author={Blöcher, Marcel and Nedderhut, Nils and Chuprikov, Pavel and Khalili, Ramin and Eugster, Patrick and Wang, Lin} }' chicago: 'Blöcher, Marcel, Nils Nedderhut, Pavel Chuprikov, Ramin Khalili, Patrick Eugster, and Lin Wang. “Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES.” In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE, n.d.' ieee: 'M. Blöcher, N. Nedderhut, P. Chuprikov, R. Khalili, P. Eugster, and L. Wang, “Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES,” presented at the IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.' mla: 'Blöcher, Marcel, et al. “Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES.” Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), IEEE.' short: 'M. Blöcher, N. Nedderhut, P. Chuprikov, R. Khalili, P. Eugster, L. Wang, in: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), IEEE, n.d.' conference: end_date: 2024-05-23 location: Vancouver, Canada name: IEEE International Conference on Computer Communications (INFOCOM) start_date: 2024-05-20 date_created: 2023-12-22T20:06:42Z date_updated: 2024-01-23T20:35:09Z department: - _id: '75' language: - iso: eng publication: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) publication_status: accepted publisher: IEEE status: public title: 'Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES' type: conference user_id: '102868' year: '2024' ... --- _id: '50807' author: - first_name: Haichuan full_name: Hu, Haichuan last_name: Hu - first_name: Fangming full_name: Liu, Fangming last_name: Liu - first_name: Qiangyu full_name: Pei, Qiangyu last_name: Pei - first_name: Yongjie full_name: Yuan, Yongjie last_name: Yuan - first_name: Zichen full_name: Xu, Zichen last_name: Xu - first_name: Lin full_name: Wang, Lin id: '102868' last_name: Wang citation: ama: "Hu H, Liu F, Pei Q, Yuan Y, Xu Z, Wang L. \U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing. In: Proceedings of the ACM Web Conference (WWW). ACM; 2024." apa: "Hu, H., Liu, F., Pei, Q., Yuan, Y., Xu, Z., & Wang, L. (2024). \U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing. Proceedings of the ACM Web Conference (WWW). ACM Web Conference (WWW), Singapore." bibtex: "@inproceedings{Hu_Liu_Pei_Yuan_Xu_Wang_2024, title={\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing}, booktitle={Proceedings of the ACM Web Conference (WWW)}, publisher={ACM}, author={Hu, Haichuan and Liu, Fangming and Pei, Qiangyu and Yuan, Yongjie and Xu, Zichen and Wang, Lin}, year={2024} }" chicago: "Hu, Haichuan, Fangming Liu, Qiangyu Pei, Yongjie Yuan, Zichen Xu, and Lin Wang. “\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing.” In Proceedings of the ACM Web Conference (WWW). ACM, 2024." ieee: "H. Hu, F. Liu, Q. Pei, Y. Yuan, Z. Xu, and L. Wang, “\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing,” presented at the ACM Web Conference (WWW), Singapore, 2024." mla: "Hu, Haichuan, et al. “\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing.” Proceedings of the ACM Web Conference (WWW), ACM, 2024." short: 'H. Hu, F. Liu, Q. Pei, Y. Yuan, Z. Xu, L. Wang, in: Proceedings of the ACM Web Conference (WWW), ACM, 2024.' conference: end_date: 2024-05-17 location: Singapore name: ACM Web Conference (WWW) start_date: 2024-05-13 date_created: 2024-01-23T20:34:27Z date_updated: 2024-01-23T20:35:20Z department: - _id: '34' - _id: '7' - _id: '75' language: - iso: eng publication: Proceedings of the ACM Web Conference (WWW) publisher: ACM status: public title: "\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing" type: conference user_id: '102868' year: '2024' ... --- _id: '53095' author: - first_name: Kamran full_name: Razavi, Kamran last_name: Razavi - first_name: Saeid full_name: Ghafouri, Saeid last_name: Ghafouri - first_name: Max full_name: Mühlhäuser, Max last_name: Mühlhäuser - first_name: Pooyan full_name: Jamshidi, Pooyan last_name: Jamshidi - first_name: Lin full_name: Wang, Lin id: '102868' last_name: Wang citation: ama: 'Razavi K, Ghafouri S, Mühlhäuser M, Jamshidi P, Wang L. Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling. In: Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024. ACM; 2024.' apa: 'Razavi, K., Ghafouri, S., Mühlhäuser, M., Jamshidi, P., & Wang, L. (2024). Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling. Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024. The 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024, Athens, Greece.' bibtex: '@inproceedings{Razavi_Ghafouri_Mühlhäuser_Jamshidi_Wang_2024, title={Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling}, booktitle={Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024}, publisher={ACM}, author={Razavi, Kamran and Ghafouri, Saeid and Mühlhäuser, Max and Jamshidi, Pooyan and Wang, Lin}, year={2024} }' chicago: 'Razavi, Kamran, Saeid Ghafouri, Max Mühlhäuser, Pooyan Jamshidi, and Lin Wang. “Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling.” In Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024. ACM, 2024.' ieee: 'K. Razavi, S. Ghafouri, M. Mühlhäuser, P. Jamshidi, and L. Wang, “Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling,” presented at the The 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024, Athens, Greece, 2024.' mla: 'Razavi, Kamran, et al. “Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling.” Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024, ACM, 2024.' short: 'K. Razavi, S. Ghafouri, M. Mühlhäuser, P. Jamshidi, L. Wang, in: Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024, ACM, 2024.' conference: end_date: 2024-04-22 location: Athens, Greece name: The 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024 start_date: 2024-04-22 date_created: 2024-03-28T12:00:49Z date_updated: 2024-03-28T12:02:23Z department: - _id: '34' - _id: '7' - _id: '75' language: - iso: eng publication: Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024 publisher: ACM status: public title: 'Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling' type: conference user_id: '102868' year: '2024' ... --- _id: '29672' author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 citation: ama: 'Schneider SB. Network and Service Coordination: Conventional and Machine Learning Approaches".; 2022. doi:10.17619/UNIPB/1-1276 ' apa: 'Schneider, S. B. (2022). Network and Service Coordination: Conventional and Machine Learning Approaches". https://doi.org/10.17619/UNIPB/1-1276 ' bibtex: '@book{Schneider_2022, title={Network and Service Coordination: Conventional and Machine Learning Approaches"}, DOI={10.17619/UNIPB/1-1276 }, author={Schneider, Stefan Balthasar}, year={2022} }' chicago: 'Schneider, Stefan Balthasar. Network and Service Coordination: Conventional and Machine Learning Approaches", 2022. https://doi.org/10.17619/UNIPB/1-1276 .' ieee: 'S. B. Schneider, Network and Service Coordination: Conventional and Machine Learning Approaches". 2022.' mla: 'Schneider, Stefan Balthasar. Network and Service Coordination: Conventional and Machine Learning Approaches". 2022, doi:10.17619/UNIPB/1-1276 .' short: 'S.B. Schneider, Network and Service Coordination: Conventional and Machine Learning Approaches", 2022.' date_created: 2022-01-31T07:08:47Z date_updated: 2022-02-18T08:17:36Z department: - _id: '75' doi: '10.17619/UNIPB/1-1276 ' language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' status: public supervisor: - first_name: Karl full_name: Holger, Karl last_name: Holger title: 'Network and Service Coordination: Conventional and Machine Learning Approaches"' type: dissertation user_id: '15504' year: '2022' ... --- _id: '30236' abstract: - lang: eng text: "Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive\r\nresults. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field.\r\n\r\nTo this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of\r\nwireless mobile networks." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Stefan full_name: Werner, Stefan last_name: Werner - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Artur full_name: Hecker, Artur last_name: Hecker - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Werner S, Khalili R, Hecker A, Karl H. mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. In: IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE; 2022.' apa: 'Schneider, S. B., Werner, S., Khalili, R., Hecker, A., & Karl, H. (2022). mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest.' bibtex: '@inproceedings{Schneider_Werner_Khalili_Hecker_Karl_2022, title={mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}, year={2022} }' chicago: 'Schneider, Stefan Balthasar, Stefan Werner, Ramin Khalili, Artur Hecker, and Holger Karl. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” In IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE, 2022.' ieee: '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.' mla: 'Schneider, Stefan Balthasar, et al. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' short: 'S.B. Schneider, S. Werner, R. Khalili, A. Hecker, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' conference: end_date: 2022-04-29 location: Budapest name: IEEE/IFIP Network Operations and Management Symposium (NOMS) start_date: 2022-04-25 date_created: 2022-03-10T18:28:14Z date_updated: 2022-03-10T18:28:19Z ddc: - '004' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2022-03-10T18:25:41Z date_updated: 2022-03-10T18:25:41Z file_id: '30237' file_name: author_version.pdf file_size: 223412 relation: main_file file_date_updated: 2022-03-10T18:25:41Z has_accepted_license: '1' keyword: - wireless mobile networks - network management - continuous control - cognitive networks - autonomous coordination - reinforcement learning - gym environment - simulation - open source language: - iso: eng oa: '1' project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' publication: IEEE/IFIP Network Operations and Management Symposium (NOMS) publisher: IEEE quality_controlled: '1' status: public title: 'mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks' type: conference user_id: '35343' year: '2022' ... --- _id: '32811' abstract: - lang: eng text: 'The decentralized nature of multi-agent systems requires continuous data exchange to achieve global objectives. In such scenarios, Age of Information (AoI) has become an important metric of the freshness of exchanged data due to the error-proneness and delays of communication systems. Communication systems usually possess dependencies: the process describing the success or failure of communication is highly correlated when these attempts are ``close'''' in some domain (e.g. in time, frequency, space or code as in wireless communication) and is, in general, non-stationary. To study AoI in such scenarios, we consider an abstract event-based AoI process $\Delta(n)$, expressing time since the last update: If, at time $n$, a monitoring node receives a status update from a source node (event $A(n-1)$ occurs), then $\Delta(n)$ is reset to one; otherwise, $\Delta(n)$ grows linearly in time. This AoI process can thus be viewed as a special random walk with resets. The event process $A(n)$ may be nonstationary and we merely assume that its temporal dependencies decay sufficiently, described by $\alpha$-mixing. We calculate moment bounds for the resulting AoI process as a function of the mixing rate of $A(n)$. Furthermore, we prove that the AoI process $\Delta(n)$ is itself $\alpha$-mixing from which we conclude a strong law of large numbers for $\Delta(n)$. These results are new, since AoI processes have not been studied so far in this general strongly mixing setting. This opens up future work on renewal processes with non-independent interarrival times.' author: - first_name: Adrian full_name: Redder, Adrian id: '52265' last_name: Redder orcid: https://orcid.org/0000-0001-7391-4688 - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Redder A, Ramaswamy A, Karl H. Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law. In: Proceedings of the 58th Allerton Conference on Communication, Control, and Computing. ; 2022.' apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law. Proceedings of the 58th Allerton Conference on Communication, Control, and Computing. 58th Allerton Conference on Communication, Control, and Computing. bibtex: '@inproceedings{Redder_Ramaswamy_Karl_2022, title={Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law}, booktitle={Proceedings of the 58th Allerton Conference on Communication, Control, and Computing}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }' chicago: Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law.” In Proceedings of the 58th Allerton Conference on Communication, Control, and Computing, 2022. ieee: 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. mla: Redder, Adrian, et al. “Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law.” Proceedings of the 58th Allerton Conference on Communication, Control, and Computing, 2022. short: 'A. Redder, A. Ramaswamy, H. Karl, in: Proceedings of the 58th Allerton Conference on Communication, Control, and Computing, 2022.' conference: name: 58th Allerton Conference on Communication, Control, and Computing date_created: 2022-08-15T09:59:17Z date_updated: 2022-11-18T09:31:19Z ddc: - '000' department: - _id: '75' has_accepted_license: '1' language: - iso: eng project: - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' publication: Proceedings of the 58th Allerton Conference on Communication, Control, and Computing status: public title: Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law type: conference user_id: '477' year: '2022' ... --- _id: '30793' author: - first_name: Adrian full_name: Redder, Adrian id: '52265' last_name: Redder orcid: https://orcid.org/0000-0001-7391-4688 - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Redder A, Ramaswamy A, Karl H. Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication. In: Proceedings of the 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications; 2022. doi:10.5220/0010845400003116' apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication. Proceedings of the 14th International Conference on Agents and Artificial Intelligence. https://doi.org/10.5220/0010845400003116 bibtex: '@inproceedings{Redder_Ramaswamy_Karl_2022, title={Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication}, DOI={10.5220/0010845400003116}, booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence}, publisher={SCITEPRESS - Science and Technology Publications}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }' chicago: Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Multi-Agent Policy Gradient Algorithms for Cyber-Physical Systems with Lossy Communication.” In Proceedings of the 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2022. https://doi.org/10.5220/0010845400003116. ieee: 'A. Redder, A. Ramaswamy, and H. Karl, “Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication,” 2022, doi: 10.5220/0010845400003116.' mla: Redder, Adrian, et al. “Multi-Agent Policy Gradient Algorithms for Cyber-Physical Systems with Lossy Communication.” Proceedings of the 14th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, 2022, doi:10.5220/0010845400003116. short: 'A. Redder, A. Ramaswamy, H. Karl, in: Proceedings of the 14th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, 2022.' date_created: 2022-04-06T07:18:36Z date_updated: 2022-11-18T09:32:14Z ddc: - '006' department: - _id: '75' doi: 10.5220/0010845400003116 file: - access_level: closed content_type: application/pdf creator: aredder date_created: 2022-08-31T07:10:13Z date_updated: 2022-08-31T07:10:13Z file_id: '33237' file_name: ICCART2022.pdf file_size: 298926 relation: main_file success: 1 file_date_updated: 2022-08-31T07:10:13Z has_accepted_license: '1' language: - iso: eng project: - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '24' name: 'NICCI-CN: Netzgewahre Regelung & regelungsgewahre Netze' - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' publication: Proceedings of the 14th International Conference on Agents and Artificial Intelligence publication_status: published publisher: SCITEPRESS - Science and Technology Publications status: public title: Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication type: conference user_id: '477' year: '2022' ... --- _id: '30790' abstract: - lang: eng text: "Iterative distributed optimization algorithms involve multiple agents that\r\ncommunicate with each other, over time, in order to minimize/maximize a global\r\nobjective. In the presence of unreliable communication networks, the\r\nAge-of-Information (AoI), which measures the freshness of data received, may be\r\nlarge and hence hinder algorithmic convergence. In this paper, we study the\r\nconvergence of general distributed gradient-based optimization algorithms in\r\nthe presence of communication that neither happens periodically nor at\r\nstochastically independent points in time. We show that convergence is\r\nguaranteed provided the random variables associated with the AoI processes are\r\nstochastically dominated by a random variable with finite first moment. This\r\nimproves on previous requirements of boundedness of more than the first moment.\r\nWe then introduce stochastically strongly connected (SSC) networks, a new\r\nstochastic form of strong connectedness for time-varying networks. We show: If\r\nfor any $p \\ge0$ the processes that describe the success of communication\r\nbetween agents in a SSC network are $\\alpha$-mixing with $n^{p-1}\\alpha(n)$\r\nsummable, then the associated AoI processes are stochastically dominated by a\r\nrandom variable with finite $p$-th moment. In combination with our first\r\ncontribution, this implies that distributed stochastic gradient descend\r\nconverges in the presence of AoI, if $\\alpha(n)$ is summable." author: - first_name: Adrian full_name: Redder, Adrian id: '52265' last_name: Redder orcid: https://orcid.org/0000-0001-7391-4688 - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: Redder A, Ramaswamy A, Karl H. Distributed gradient-based optimization in the presence of dependent  aperiodic communication. arXiv:220111343. Published online 2022. apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Distributed gradient-based optimization in the presence of dependent  aperiodic communication. In arXiv:2201.11343. bibtex: '@article{Redder_Ramaswamy_Karl_2022, title={Distributed gradient-based optimization in the presence of dependent  aperiodic communication}, journal={arXiv:2201.11343}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }' chicago: Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Distributed Gradient-Based Optimization in the Presence of Dependent  Aperiodic Communication.” ArXiv:2201.11343, 2022. ieee: A. Redder, A. Ramaswamy, and H. Karl, “Distributed gradient-based optimization in the presence of dependent  aperiodic communication,” arXiv:2201.11343. 2022. mla: Redder, Adrian, et al. “Distributed Gradient-Based Optimization in the Presence of Dependent  Aperiodic Communication.” ArXiv:2201.11343, 2022. short: A. Redder, A. Ramaswamy, H. Karl, ArXiv:2201.11343 (2022). date_created: 2022-04-06T06:53:38Z date_updated: 2022-11-18T09:33:01Z department: - _id: '75' external_id: arxiv: - '2201.11343' language: - iso: eng project: - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' publication: arXiv:2201.11343 status: public title: Distributed gradient-based optimization in the presence of dependent aperiodic communication type: preprint user_id: '477' year: '2022' ... --- _id: '30791' abstract: - lang: eng text: "We present sufficient conditions that ensure convergence of the multi-agent\r\nDeep Deterministic Policy Gradient (DDPG) algorithm. It is an example of one of\r\nthe most popular paradigms of Deep Reinforcement Learning (DeepRL) for tackling\r\ncontinuous action spaces: the actor-critic paradigm. In the setting considered\r\nherein, each agent observes a part of the global state space in order to take\r\nlocal actions, for which it receives local rewards. For every agent, DDPG\r\ntrains a local actor (policy) and a local critic (Q-function). The analysis\r\nshows that multi-agent DDPG using neural networks to approximate the local\r\npolicies and critics converge to limits with the following properties: The\r\ncritic limits minimize the average squared Bellman loss; the actor limits\r\nparameterize a policy that maximizes the local critic's approximation of\r\n$Q_i^*$, where $i$ is the agent index. The averaging is with respect to a\r\nprobability distribution over the global state-action space. It captures the\r\nasymptotics of all local training processes. Finally, we extend the analysis to\r\na fully decentralized setting where agents communicate over a wireless network\r\nprone to delays and losses; a typical scenario in, e.g., robotic applications." author: - first_name: Adrian full_name: Redder, Adrian id: '52265' last_name: Redder orcid: https://orcid.org/0000-0001-7391-4688 - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: Redder A, Ramaswamy A, Karl H. Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. arXiv:220100570. Published online 2022. apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. In arXiv:2201.00570. bibtex: '@article{Redder_Ramaswamy_Karl_2022, title={Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms}, journal={arXiv:2201.00570}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }' chicago: Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms.” ArXiv:2201.00570, 2022. ieee: A. Redder, A. Ramaswamy, and H. Karl, “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms,” arXiv:2201.00570. 2022. mla: Redder, Adrian, et al. “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms.” ArXiv:2201.00570, 2022. short: A. Redder, A. Ramaswamy, H. Karl, ArXiv:2201.00570 (2022). date_created: 2022-04-06T06:53:52Z date_updated: 2022-11-18T09:33:42Z department: - _id: '75' external_id: arxiv: - '2201.00570' language: - iso: eng project: - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' publication: arXiv:2201.00570 status: public title: Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms type: preprint user_id: '477' year: '2022' ...