  We study multi-player general-sum Markov games with one of the players
designated as the leader and the other players regarded as followers. In
particular, we focus on the class of games where the followers are myopic,
i.e., they aim to maximize their instantaneous rewards. For such a game, our
goal is to find a Stackelberg-Nash equilibrium (SNE), which is a policy pair
$(\pi^*, \nu^*)$ such that (i) $\pi^*$ is the optimal policy for the leader
when the followers always play their best response, and (ii) $\nu^*$ is the
best response policy of the followers, which is a Nash equilibrium of the
followers' game induced by $\pi^*$. We develop sample-efficient reinforcement
learning (RL) algorithms for solving for an SNE in both online and offline
settings. Our algorithms are optimistic and pessimistic variants of
least-squares value iteration, and they are readily able to incorporate
function approximation tools in the setting of large state spaces. Furthermore,
for the case with linear function approximation, we prove that our algorithms
achieve sublinear regret and suboptimality under online and offline setups
respectively. To the best of our knowledge, we establish the first provably
efficient RL algorithms for solving for SNEs in general-sum Markov games with
myopic followers.
