Tatiana Tatarenko

Dr. rer. nat. Tatiana Tatarenko

Dissertation: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems, heute: TU Darmstadt, Regelungsmethoden und Robotik, Tätigkeit: Gruppenleiterin


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Springe zu: 2019 | 2017 | 2016 | 2015 | 2014 | 2013
Anzahl der Einträge: 17.


Tatarenko, Tatiana ; Zimmermann, Jan ; Willert, Volker ; Adamy, Jürgen (2019):
Penalized Push-Sum Algorithm for Constrained Distributed Optimization with Application to Energy Management in Smart Grid.
In: 58th Conference on Decision and Control (CDC), Nice, France, December 11.-13., 2019, [Online-Edition: https://www1.rmr.tu-darmstadt.de/pdf/zimmermann-2-2019.pdf],

Zimmermann, Jan ; Tatarenko, Tatiana (2019):
Optimales Energie-Management über verteilte, beschränkte Gradientenverfahren.
In: at - Automatisierungstechnik, 67 (11), S. 922-935, ISSN 2196-677X (Online) bzw. 0178-2312 (Print),
DOI: 10.1515/auto-2019-0064,
[Online-Edition: https://www1.rmr.tu-darmstadt.de/pdf/zimmermann-2019.pdf],


Nedić, A. ; Tatarenko, Tatiana (2017):
On stochastic proximal-point method for convex-composite optimization.
In: 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton, USA, October, 2017, [Konferenzveröffentlichung]

Tatarenko, Tatiana ; Touri, Behrouz (2017):
Non-Convex Distributed Optimization.
In: IEEE Transactions on Automatic Control, 62 (8), S. 3744-3757, [Artikel]

Tatarenko, Tatiana ; Kamgarpour, M. (2017):
Payoff-Based Approach to Learning Nash Equilibria in Convex Games.
In: 20th IFAC World Congress, Toulouse, France, July, 2017, [Konferenzveröffentlichung]

Tatarenko, Tatiana (2017):
Independent Log-Linear Learning in Potential Games with Continuous Actions.
In: IEEE Transactions on Control of Network Systems, [Artikel]

Tatarenko, Tatiana (2017):
Game-theoretic learning and distributed optimization in memoryless multi-agent systems.
Darmstadt, TU Darmstadt, [Dissertation]


Tatarenko, Tatiana (2016):
Stochastic Payoff-Based Learning in Multi-Agent Systems Modeled by Means of Potential Games.
In: Conference on Decision and Control, Las Vegas, USA, December 12-15, 2016, [Konferenzveröffentlichung]

Tatarenko, Tatiana ; Touri, Behrouz (2016):
On Local Analysis of Distributed Optimization.
In: American Control Conference, Boston, MA, USA, July 6-8, 2016, [Konferenzveröffentlichung]

Tatarenko, Tatiana (2016):
Stochastic Stability of Potential Function Maximizers in Continuous Version of Independent Log-Linear Learning.
In: European Control Conference, Aalborg, Denmark, June 29-July 1, 2016, [Konferenzveröffentlichung]


Tatarenko, Tatiana (2015):
1-Recall Reinforcement Learning Leading to an Optimal Equilibrium in Potential Games with Discrete and Continuous Actions.
In: IEEE Conference on Decision and Control 2015, Osaka, Japan, Dezember 14-17, [Konferenzveröffentlichung]

Tatarenko, Tatiana (2015):
Synchronous learning of efficient Nash equilibria in potential games with uncoupled dynamics and memoryless players.
In: European Control Conference 2015, Linz, Österreich, Juli 15-17, [Konferenzveröffentlichung]


Tatarenko, Tatiana (2014):
Proving Convergence of Log-Linear Learning in Potential Games.
In: American Control Conference, Portland, Oregon, USA, 2014, June 4 - 6, [Konferenzveröffentlichung]

Tatarenko, Tatiana ; Popov, Ivan (2014):
Nash Equilibrium Flow in a Routing Game with Random Queues.
In: 26th Chinese Control and Decision Conference, Changsha, China, 2014, May 31 - June 2, [Konferenzveröffentlichung]

Tatarenko, Tatiana (2014):
A Game Theoretic and Control Theoretic Approach to Incentive-Based Demand Management in Smart Grids.
In: 22nd Mediterranean Conference on Control & Automation, Palermo, Italy, June 16 - 19, [Konferenzveröffentlichung]


Tatarenko, Tatiana (2013):
On Convergence of Log-Linear Learning in Potential Games.
In: UECE Meetings on Game Theory and Applications, Lisbon, Portugal, 7.-9. Nov. 2013, [Konferenzveröffentlichung]

Zubkov, A. M. ; Tatarenko, Tatiana (2013):
The Equivalence between the Schur Order and the Stochastic Orders Generated by the Scheme of Allocation of Particles into Cells.
In: Mathematical Notes, 93 (1), S. 69-74, [Artikel]

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