Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
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A natural solution concept for many multiagent settings is the Stackelberg equilibrium, under which a “leader” agent selects a strategy that maximizes its own payoff assuming the “follower” chooses their best response to this strategy. Recent work has presented asymmetric learning updates that can be shown to converge to the differential Stackelberg equilibria of two-player differentiable games. These updates are “coupled” in the sense that the leader requires some information about the follower's payoff function. Such coupled learning rules cannot be applied to ad hoc interactive learning settings, and can be computationally impractical even in centralized training settings where the follower's payoffs are known. In this work, we present an “uncoupled” learning process under which each player's learning update only depends on their observations of the other's behavior. We prove that this process converges to a local Stackelberg equilibrium under similar conditions as previous coupled methods. We conclude with a discussion of the potential applications of our approach to human–AI cooperation and multi-agent reinforcement learning.
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