Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

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 …

Policy gradient methods are among the most effective methods for large-scale reinforcement
learning, and their empirical success has prompted several works that develop the …

This chapter presents machine learning techniques and deep reinforcement learning-based
algorithms for the efficient resolution of nonlinear partial differential equations and dynamic …

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 …

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 …

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 …

Reinforcement learning (RL) has gained increasing popularity for resource management in
cloud services such as serverless computing. As self-interested users compete for shared …

We present APAC-Net, an alternating population and agent control neural network for
solving stochastic mean-field games (MFGs). Our algorithm is geared toward high …

We study a heterogeneous agent macroeconomic model with an infinite number of
households and firms competing in a labor market. Each household earns income and …