  We investigate statistical uncertainty quantification for reinforcement
learning (RL) and its implications in exploration policy. Despite ever-growing
literature on RL applications, fundamental questions about inference and error
quantification, such as large-sample behaviors, appear to remain quite open. In
this paper, we fill in the literature gap by studying the central limit theorem
behaviors of estimated Q-values and value functions under various RL settings.
In particular, we explicitly identify closed-form expressions of the asymptotic
variances, which allow us to efficiently construct asymptotically valid
confidence regions for key RL quantities. Furthermore, we utilize these
asymptotic expressions to design an effective exploration strategy, which we
call Q-value-based Optimal Computing Budget Allocation (Q-OCBA). The policy
relies on maximizing the relative discrepancies among the Q-value estimates.
Numerical experiments show superior performances of our exploration strategy
than other benchmark policies.

Machine Learning;Machine Learning