  Function approximation (FA) has been a critical component in solving large
zero-sum games. Yet, little attention has been given towards FA in solving
\textit{general-sum} extensive-form games, despite them being widely regarded
as being computationally more challenging than their fully competitive or
cooperative counterparts. A key challenge is that for many equilibria in
general-sum games, no simple analogue to the state value function used in
Markov Decision Processes and zero-sum games exists. In this paper, we propose
learning the \textit{Enforceable Payoff Frontier} (EPF) -- a generalization of
the state value function for general-sum games. We approximate the optimal
\textit{Stackelberg extensive-form correlated equilibrium} by representing EPFs
with neural networks and training them by using appropriate backup operations
and loss functions. This is the first method that applies FA to the Stackelberg
setting, allowing us to scale to much larger games while still enjoying
performance guarantees based on FA error. Additionally, our proposed method
guarantees incentive compatibility and is easy to evaluate without having to
depend on self-play or approximate best-response oracles.
