The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability
This addresses challenges in multi-agent systems like economics, though it appears incremental as it builds on existing reinforcement learning frameworks.
The paper tackles the problem of learning optimal policies in online strategic interactions with information asymmetry and knowledge transportability, presenting an algorithm that achieves an ε-optimal policy with a sample complexity of O(1/ε^2).
Information asymmetry is a pervasive feature of multi-agent systems, especially evident in economics and social sciences. In these settings, agents tailor their actions based on private information to maximize their rewards. These strategic behaviors often introduce complexities due to confounding variables. Simultaneously, knowledge transportability poses another significant challenge, arising from the difficulties of conducting experiments in target environments. It requires transferring knowledge from environments where empirical data is more readily available. Against these backdrops, this paper explores a fundamental question in online learning: Can we employ non-i.i.d. actions to learn about confounders even when requiring knowledge transfer? We present a sample-efficient algorithm designed to accurately identify system dynamics under information asymmetry and to navigate the challenges of knowledge transfer effectively in reinforcement learning, framed within an online strategic interaction model. Our method provably achieves learning of an $ε$-optimal policy with a tight sample complexity of $O(1/ε^2)$.