Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL

We study the sample complexity of reinforcement learning (RL) in Mean-Field Games (MFGs) with model-based function approximation that requires strategic exploration to find a Nash Equilibrium policy. We introduce the Partial Model-Based Eluder Dimension (P-MBED), a more effective notion to characterize the model class complexity. Notably, P-MBED measures the complexity of the single-agent model class converted from the given mean-field model class, and potentially, can be exponentially lower than the MBED proposed by \citet{huang2023statistical}. We contribute a model elimination algorithm …

agent approximation class complexity cs.ai cs.gt cs.lg equilibrium exploration function games mean nash equilibrium notion policy reinforcement reinforcement learning sample stat.ml study