  As AI becomes more prevalent throughout society, effective methods of
integrating humans and AI systems that leverage their respective strengths and
mitigate risk have become an important priority. In this paper, we introduce
the paradigm of super reinforcement learning that takes advantage of Human-AI
interaction for data driven sequential decision making. This approach utilizes
the observed action, either from AI or humans, as input for achieving a
stronger oracle in policy learning for the decision maker (humans or AI). In
the decision process with unmeasured confounding, the actions taken by past
agents can offer valuable insights into undisclosed information. By including
this information for the policy search in a novel and legitimate manner, the
proposed super reinforcement learning will yield a super-policy that is
guaranteed to outperform both the standard optimal policy and the behavior one
(e.g., past agents' actions). We call this stronger oracle a blessing from
human-AI interaction. Furthermore, to address the issue of unmeasured
confounding in finding super-policies using the batch data, a number of
nonparametric and causal identifications are established. Building upon on
these novel identification results, we develop several super-policy learning
algorithms and systematically study their theoretical properties such as
finite-sample regret guarantee. Finally, we illustrate the effectiveness of our
proposal through extensive simulations and real-world applications.
