Published in ---, 2024
This paper investigates the vulnerability of RL (specifically independent Q-learning) algorithms used for defense in the context of stochastic security games with linear influence networks.
Published in IEEE L-CSS, 2024
In this paper, we explore the susceptibility of the Q-learning algorithm (a classical and widely used reinforcement learning method) to strategic manipulation of sophisticated opponents in games.
Published in arXiv, 2023
This paper presents new families of algorithms for the repeated play of two-agent (near) zero-sum games and two-agent zero-sum stochastic games.