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We study the problem of online learning in a two-player decentralized cooperative Stackelberg game. In each round, the leader first takes an action, followed by the follower who takes their action after observing the leader’s move. The goal of the leader is to learn to minimize the cumulative regret based on the history of interactions. Differing from the traditional formulation of repeated Stackelberg games, we assume the follower is omniscient, with full knowledge of the true reward, and that they always best-respond to the leader’s actions. We analyze the sample complexity of regret minimization in this repeated Stackelberg game. We show that depending on the reward structure, the existence of the omniscient follower may change the sample complexity drastically, from constant to exponential, even for linear cooperative Stackelberg games.
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No-Regret Learning in Dynamic Stackelberg Games
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 trategies. 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 is then 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 an online learning algorithm that, with high probability, is no-regret, i.e., 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.
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- Award ID(s):
- 1652113
- PAR ID:
- 10508410
- Publisher / Repository:
- IEEE TRANSACTIONS ON AUTOMATIC CONTROL
- Date Published:
- Journal Name:
- IEEE Transactions on Automatic Control
- Volume:
- 69
- Issue:
- 3
- ISSN:
- 0018-9286
- Page Range / eLocation ID:
- 1418 - 1431
- Subject(s) / Keyword(s):
- Iterative learning control, no-regret learning, online learning, Stackelberg games, stochastic games
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TAC.2023.3330797
