  Optimizing strategic decisions (a.k.a. computing equilibrium) is key to the
success of many non-cooperative multi-agent applications. However, in many
real-world situations, we may face the exact opposite of this game-theoretic
problem -- instead of prescribing equilibrium of a given game, we may directly
observe the agents' equilibrium behaviors but want to infer the underlying
parameters of an unknown game. This research question, also known as inverse
game theory, has been studied in multiple recent works in the context of
Stackelberg games. Unfortunately, existing works exhibit quite negative
results, showing statistical hardness and computational hardness, assuming
follower's perfectly rational behaviors. Our work relaxes the perfect
rationality agent assumption to the classic quantal response model, a more
realistic behavior model of bounded rationality. Interestingly, we show that
the smooth property brought by such bounded rationality model actually leads to
provably more efficient learning of the follower utility parameters in general
Stackelberg games. Systematic empirical experiments on synthesized games
confirm our theoretical results and further suggest its robustness beyond the
strict quantal response model.
