Computationally efficient learning in large-scale games: Sampled fictitious play revisited

Fictitious Play (FP) is a popular algorithm known to achieve Nash equilibrium learning in certain large-scale games. However, for games with many players, the computational demands of the FP algorithm can be prohibitive. Sampled FP (SFP) is a variant of FP that mitigates computational demands via a Monte Carlo approach. While SFP does mitigate the complexity of FP, it can be shown that SFP still uses information in an inefficient manner. The paper generalizes the SFP convergence result and studies a stochastic-approximation-based variant that significantly reduces the complexity of SFP.