This section illustrates that (7) holds.

In contrast, we design a discrete-time algorithm and derive its convergence rate solely under weakly monotone conditions.

Most existing results only focus on online settings, in which agents can interact with the environment during training.

We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs), where the goal is to imitate the behavior of a population of agents following a Nash equilibrium policy according to some unknown payoff function. IL in MFGs presents new challenges compared to single-agent IL, particularly when both the reward function and the transition kernel depend on the population distribution. In this paper, departing from the existing literature on IL for MFGs, we introduce a new solution concept called the Nash imitation gap. Then we show that when only the reward depends on the population distribution, IL in MFGs can be reduced to single-agent IL with similar guarantees. However, when the dynamics is population-dependent, we provide a novel upper-bound that suggests IL is harder in this setting. To address this issue, we propose a new adversarial formulation where the reinforcement learning problem is replaced by a mean-field control (MFC) problem, suggesting progress in IL within MFGs may have to build upon MFC.

This section illustrates that (7) holds.

Most existing results only focus on online settings, in which agents can interact with the environment during training.

In such framework, all players are identical, anonymous ( i.e., they are not identifiable) and have symmetric interests.