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On the effect of clock offsets and quantization on learning-based adversarial games

https://doi.org/10.1016/j.automatica.2024.111762

Fotiadis, Filippos ; Kanellopoulos, Aris ; Vamvoudakis, Kyriakos G ; Hugues, Jerome ( September 2024 , Automatica)

Free, publicly-accessible full text available September 1, 2025
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

Haeri_Boroujeni, Sayed_Pedram ; Razi, Abolfazl ; Khoshdel, Sahand ; Afghah, Fatemeh ; Coen, Janice L. ; O’Neill, Leo ; Fule, Peter ; Watts, Adam ; Kokolakis, Nick_Marios T ; Vamvoudakis, Kyriakos G. ( August 2024 , Information Fusion)

Free, publicly-accessible full text available August 1, 2025
Data-driven controller and multi-gradient search algorithm for morphing airfoils in high Reynolds number flows

https://doi.org/10.1016/j.ast.2024.109106

Magalhães_Júnior, José M ; Halila, Gustavo LO ; Vamvoudakis, Kyriakos G ( May 2024 , Aerospace Science and Technology)

Free, publicly-accessible full text available May 1, 2025
Data-Driven Actuator Allocation for Actuator Redundant Systems

https://doi.org/10.1109/TAC.2023.3328993

Fotiadis, Filippos ; Vamvoudakis, Kyriakos G ; Jiang, Zhong-Ping ( April 2024 , IEEE Transactions on Automatic Control)

Free, publicly-accessible full text available April 1, 2025
Learning-Based Quantum Control for Optimal Pure State Manipulation

https://doi.org/10.1109/LCSYS.2024.3409671

Chen, Anthony Siming ; Herrmann, Guido ; Vamvoudakis, Kyriakos G ; Vijayan, Jayadev ( January 2024 , IEEE Control Systems Letters)

Free, publicly-accessible full text available January 1, 2025
Adaptive Optimal Output Regulation of Discrete-Time Linear Systems: A Reinforcement Learning Approach

https://doi.org/10.1109/CDC49753.2023.10383803

Chakraborty, Sayan ; Gao, Weinan ; Vamvoudakis, Kyriakos G ; Jiang, Zhong-Ping ( December 2023 , IEEE)

In this paper, we solve the optimal output regulation of discrete-time systems without precise knowledge of the system model. Drawing inspiration from reinforcement learning and adaptive dynamic programming, a data-driven solution is developed that enables asymptotic tracking and disturbance rejection. Notably, it is discovered that the proposed approach for discrete-time output regulation differs from the continuous-time approach in terms of the persistent excitation condition required for policy iteration to be unique and convergent. To address this issue, a new persistent excitation condition is introduced to ensure both uniqueness and convergence of the data-driven policy iteration. The efficacy of the proposed methodology is validated by an inverted pendulum on a cart example.
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Free, publicly-accessible full text available December 13, 2024
Convergence Rates of Online Critic Value Function Approximation in Native Spaces

https://doi.org/10.1109/LCSYS.2024.3417178

Niu, Shengyuan ; Bouland, Ali ; Wang, Haoran ; Fotiadis, Filippos ; Kurdila, Andrew ; L’Afflitto, Andrea ; Paruchuri, Sai Tej ; Vamvoudakis, Kyriakos G ( January 2024 , IEEE Control Systems Letters)

Free, publicly-accessible full text available January 1, 2025
Intelligent Players in a Fictitious Play Framework

https://doi.org/10.1109/TAC.2023.3266505

Vundurthy, Bhaskar ; Kanellopoulos, Aris ; Gupta, Vijay ; Vamvoudakis, Kyriakos G. ( August 2023 , IEEE Transactions on Automatic Control)

Full Text Available
Cooperative Finitely Excited Learning for Dynamical Games

https://doi.org/10.1109/TCYB.2023.3274908

Yang, Yongliang ; Modares, Hamidreza ; Vamvoudakis, Kyriakos G. ; Lewis, Frank L. ( August 2023 , IEEE Transactions on Cybernetics)

Full Text Available
Non-Equilibrium Learning in Stochastic Games

https://doi.org/10.23919/ACC55779.2023.10156061

Vamvoudakis, Kyriakos G. ( May 2023 , 2023 American Control Conference (ACC))

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