Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …

Matrix games like Prisoner's Dilemma have guided research on social dilemmas for
decades. However, they necessarily treat the choice to cooperate or defect as an atomic …

Efficient use of resources while ensuring quality services points the attention of Home Health
Care structures (HHC). HHC structures propose keeping at home patients who do not …

SARSA is an on-policy algorithm to learn a Markov decision process policy in reinforcement
learning. We investigate the SARSA algorithm with linear function approximation under the …

In this work, we develop provably efficient reinforcement learning algorithms for two-player
zero-sum Markov games with simultaneous moves. We consider a family of Markov games …

We study the global convergence of policy optimization for finding the Nash equilibria (NE)
in zero-sum linear quadratic (LQ) games. To this end, we first investigate the landscape of …

We study online reinforcement learning in average-reward stochastic games (SGs). An SG
models a two-player zero-sum game in a Markov environment, where state transitions and …

This paper considers two-player zero-sum finite-horizon Markov games with simultaneous
moves. The study focuses on the challenging settings where the value function or the model …

The Nash equilibrium is an important concept in game theory. It describes the least
exploitability of one player from any opponents. We combine game theory, dynamic …