aiqbench analysis
This research advances inverse reinforcement learning by proposing algorithms to infer a demonstrator's internal suboptimality gaps from randomized policies like Thompson sampling. The study establishes theoretical conditions for reliable inversion and highlights a trade-off between stability and coverage when the agent concentrates on specific actions. These findings provide critical insights for modeling complex decision-making processes in advanced machine learning systems.
Learning from a Bandit Learner
arXiv:2610.26103v1 Announce Type: new Abstract: Classical inverse bandit methods typically aim to recover true reward parameters. In contrast, we infer a demonstrator's subjective beliefs, specifically its internal estimates of suboptimality gaps. We consider no-regret demonstrators that follow randomized policies, such as Thompson sampling, and characterize a family of algorithms for which per-step inversion is possible. This enables gap estimates to be recovered directly from the arm-selection distribution. Our analysis identifies an inherent stability-coverage trade-off: inversion becomes ill-conditioned as the demonstrator concentrates its probability mass on a single arm, while the number of informative rounds depends on the rate at which its policy converges. We establish conditions under which inversion is reliable and provide theoretical guarantees on the resulting estimation error. Experiments on simulated data support the analysis and illustrate how these conditions affect gap recovery in practice.
Abstract:Classical inverse bandit methods typically aim to recover true reward parameters. In contrast, we infer a demonstrator's subjective beliefs, specifically its internal estimates of suboptimality gaps. We consider no-regret demonstrators that follow randomized policies, such as Thompson sampling, and characterize a family of algorithms for which per-step inversion is possible. This enables gap estimates to be recovered directly from the arm-selection distribution. Our analysis identifies an inherent stability-coverage trade-off: inversion becomes ill-conditioned as the demonstrator concentrates its probability mass on a single arm, while the number of informative rounds depends on the rate at which its policy converges. We establish conditions under which inversion is reliable and provide theoretical guarantees on the resulting estimation error. Experiments on simulated data support the analysis and illustrate how these conditions affect gap recovery in practice.
Machine Learning (cs.LG)
arXiv:2610.26103 [cs.LG]
(or arXiv:2610.26103v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.26103 Focus to learn more arXiv-issued DOI via DataCite
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