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.

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

Redder A, Ramaswamy A, Karl H. Distributed gradient-based optimization in the presence of dependent  aperiodic communication. arXiv:220111343. Published online 2022.

Redder A, Ramaswamy A, Karl H. Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. arXiv:220100570. Published online 2022.

Redder A, Ramaswamy A, Karl H. Practical Network Conditions for the Convergence of Distributed Optimization. IFAC-PapersOnLine. 2022;55(13):133–138.

Ramaswamy A, Bhatnagar S. Analyzing Approximate Value Iteration Algorithms. Mathematics of Operations Research (to appear). Published online 2021.

Drees JP, Gupta P, Hüllermeier E, et al. Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! 14th ACM Workshop on Artificial Intelligence and Security. Published online 2021.

Ramaswamy A, Hüllermeier E. Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. IEEE Transactions on Artificial Intelligence (to appear). Published online 2021.

Afifi H, Ramaswamy A, Karl H. 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). Montreal, Canada; 2021.

Afifi H, Ramaswamy A, Karl H. 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.

Ramaswamy A, Redder A, Quevedo DE. Distributed optimization over time-varying networks with stochastic information delays. IEEE Transactions on Automatic Control. Published online 2021:1-1. doi:10.1109/TAC.2021.3108492

Ramaswamy A. DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme. In: 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). ; 2020:54-62.

Heid SH, Ramaswamy A, Hüllermeier E. Constrained Multi-Agent Optimization with Unbounded Information Delay. In: Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020. Vol 26. ; 2020:247.

Ramaswamy A, Bhatnagar S, Quevedo DE. Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning. IEEE Transactions on Automatic Control. Published online 2020.

König J, Malberg S, Martens M, Niehaus S, Krohn-Grimberghe A, Ramaswamy A. Multi-stage reinforcement learning for object detection. In: Science and Information Conference. ; 2019:178-191.

Leong AS, Ramaswamy A, Quevedo DE, Karl H, Shi L. Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems. Automatica. 2019. doi:10.1016/j.automatica.2019.108759

Redder A, Ramaswamy A, Quevedo D. Deep reinforcement learning for scheduling in large-scale networked control systems. In: Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems. ; 2019.

Ramaswamy A, Bhatnagar S. Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control. 2018;64(6):2614-2620.

Demirel B, Ramaswamy A, Quevedo DE, Karl H. Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters. 2018;2(4):737-742.

Ramaswamy A, Bhatnagar S. Analysis of gradient descent methods with nondiminishing bounded errors. IEEE Transactions on Automatic Control. 2017;63(5):1465-1471.