  In a Stackelberg game, a leader commits to a randomized strategy, and a
follower chooses their best strategy in response. We consider an extension of a
standard Stackelberg game, called a discrete-time dynamic Stackelberg game,
that has an underlying state space that affects the leader's rewards and
available strategies and evolves in a Markovian manner depending on both the
leader and follower's selected strategies. Although standard Stackelberg games
have been utilized to improve scheduling in security domains, their deployment
is often limited by requiring complete information of the follower's utility
function. In contrast, we consider scenarios where the follower's utility
function is unknown to the leader; however, it can be linearly parameterized.
Our objective then is to provide an algorithm that prescribes a randomized
strategy to the leader at each step of the game based on observations of how
the follower responded in previous steps. We design a no-regret learning
algorithm that, with high probability, achieves a regret bound (when compared
to the best policy in hindsight) which is sublinear in the number of time
steps; the degree of sublinearity depends on the number of features
representing the follower's utility function. The regret of the proposed
learning algorithm is independent of the size of the state space and polynomial
in the rest of the parameters of the game. We show that the proposed learning
algorithm outperforms existing model-free reinforcement learning approaches.
