Multi-agent learning (MAL) studies how agents learn to behave optimally and adaptively
from their experience when interacting with other agents in dynamic environments. The …

We study multi-agent reinforcement learning (MARL) in infinite-horizon discounted zero-sum
Markov games. We focus on the practical but challenging setting of decentralized MARL …

Reinforcement learning (RL) has recently achieved tremendous successes in many artificial
intelligence applications. Many of the forefront applications of RL involve multiple agents …

We study infinite-horizon discounted two-player zero-sum Markov games, and develop a
decentralized algorithm that provably converges to the set of Nash equilibria under self-play …

Multiagent decision making over networks has recently attracted an exponentially growing
number of researchers from the systems and control community. The area has gained …

We study the problem of learning a Nash equilibrium (NE) in Markov games which is a
cornerstone in multi-agent reinforcement learning (MARL). In particular, we focus on infinite …

Learning in stochastic games is a notoriously difficult problem because, in addition to each
other's strategic decisions, the players must also contend with the fact that the game itself …

The works of (Daskalakis et al., 2009, 2022; ** et al., 2022; Deng et al., 2023) indicate that
computing Nash equilibria in multi-player Markov games is a computationally hard task. This …

We study a new class of Markov games,\textit {(multi-player) zero-sum Markov Games} with
{\it Networked separable interactions}(zero-sum NMGs), to model the local interaction …

In this work, we study two-player zero-sum stochastic games and develop a variant of the
smoothed best-response learning dynamics that combines independent learning dynamics …