Abstract Mean Field Games (MFGs) have been introduced to efficiently approximate games
with very large populations of strategic agents. Recently, the question of learning equilibria …

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 …

Mean-field games have been used as a theoretical tool to obtain an approximate Nash
equilibrium for symmetric and anonymous $ N $-player games. However, limiting …

Concave Utility Reinforcement Learning (CURL) extends RL from linear to concave utilities
in the occupancy measure induced by the agent's policy. This encompasses not only RL but …

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 …

We study infinite horizon discounted mean field control (MFC) problems with common noise
through the lens of mean field Markov decision processes (MFMDP). We allow the agents to …

We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online
Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash …

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 …

Abstract We present a Reinforcement Learning (RL) algorithm to solve infinite horizon
asymptotic Mean Field Game (MFG) and Mean Field Control (MFC) problems. Our approach …