In this work, we study the concentration behavior of a stochastic
approx...
We introduce a class of networked Markov potential games where agents ar...
We study two-player zero-sum stochastic games, and propose a form of
ind...
We study a multi-agent reinforcement learning (MARL) problem where the a...
In this work, we study policy-based methods for solving the reinforcemen...
Q-learning with function approximation is one of the most empirically
su...
Stochastic approximation (SA) and stochastic gradient descent (SGD)
algo...
In temporal difference (TD) learning, off-policy sampling is known to be...
In this paper, we develop a novel variant of off-policy natural actor-cr...
Unmanned aerial vehicles or drones are becoming increasingly popular due...
In this paper, we provide finite-sample convergence guarantees for an
of...
This paper develops an unified framework to study finite-sample converge...
Stochastic Approximation (SA) is a popular approach for solving fixed po...
In this paper, we consider the model-free reinforcement learning problem...