Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity

arXiv:2404.07266v1 Announce Type: new
Abstract: We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information. These demonstrations can be viewed as solving related but slightly different tasks than what the learner faces. This setting arises in many application domains, such as self-driving cars, healthcare, and finance, where expert demonstrations are made using contextual information, which is not recorded in the data available to the learning agent. We model …

abstract application arxiv cs.lg decision decision making decisions domains expert experts information making study tasks type