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

Mean Field Games (MFG) have been introduced to tackle games with a large number of
competing players. Considering the limit when the number of players is infinite, Nash …

The recent mean field game (MFG) formalism facilitates otherwise intractable computation of
approximate Nash equilibria in many-agent settings. In this paper, we consider discrete-time …

Non-cooperative and cooperative games with a very large number of players have many
applications but remain generally intractable when the number of players increases …

We study reinforcement learning in mean-field games. To achieve the Nash equilibrium,
which consists of a policy and a mean-field state, existing algorithms require obtaining the …

In this paper, we introduce a regularized mean-field game and study learning of this game
under an infinite-horizon discounted reward function. Regularization is introduced by adding …

Abstract Mean-Field Multi-Agent Reinforcement Learning (MF-MARL) is attractive in the
applications involving a large population of homogeneous agents, as it exploits the …

Multiagent reinforcement learning algorithms have not been widely adopted in large scale
environments with many agents as they often scale poorly with the number of agents. Using …

We propose a reinforcement learning algorithm for stationary mean-field games, where the
goal is to learn a pair of mean-field state and stationary policy that constitutes the Nash …

Condition-based maintenance (CBM) of hydropower generation unit (HPGU) is of great
significance for the intelligent operation and maintenance of hydropower station. The …