6 papers
Decentralized Optimal Equilibrium Learning in Stochastic Games via Single-bit Feedback
Seref Taha Kiremitci, Ahmed Said Donmez, Muhammed O. Sayin
We study decentralized equilibrium selection in stochastic games under severe information and communication constraints. In such settings, convergence to equilibrium alone is insuf…
Actor-Dual-Critic Dynamics for Zero-sum and Identical-Interest Stochastic Games
Ahmed Said Donmez, Yuksel Arslantas, Muhammed O. Sayin
We propose a novel independent and payoff-based learning framework for stochastic games that is model-free, game-agnostic, and gradient-free. The learning dynamics follow a best-re…
Omniscient Attacker in Stochastic Security Games with Interdependent Nodes
Yuksel Arslantas, Ahmed Said Donmez, Ege Yuceel +1
The adoption of reinforcement learning for critical infrastructure defense introduces a vulnerability where sophisticated attackers can strategically exploit the defense algorithm'…
Achieving Pareto Optimality in Games via Single-bit Feedback
Seref Taha Kiremitci, Ahmed Said Donmez, Muhammed O. Sayin
Efficient coordination in multi-agent systems often incurs high communication overhead or slow convergence rates, making scalable welfare optimization difficult. We propose Single-…
Generalized Individual Q-learning for Polymatrix Games with Partial Observations
Ahmed Said Donmez, Muhammed O. Sayin
This paper addresses the challenge of limited observations in non-cooperative multi-agent systems where agents can have partial access to other agents' actions. We present the gene…
Team-Fictitious Play for Reaching Team-Nash Equilibrium in Multi-team Games
Ahmed Said Donmez, Yuksel Arslantas, Muhammed O. Sayin
Multi-team games, prevalent in robotics and resource management, involve team members striving for a joint best response against other teams. Team-Nash equilibrium (TNE) predicts t…