  In offline reinforcement learning (RL) an optimal policy is learnt solely
from a priori collected observational data. However, in observational data,
actions are often confounded by unobserved variables. Instrumental variables
(IVs), in the context of RL, are the variables whose influence on the state
variables are all mediated through the action. When a valid instrument is
present, we can recover the confounded transition dynamics through
observational data. We study a confounded Markov decision process where the
transition dynamics admit an additive nonlinear functional form. Using IVs, we
derive a conditional moment restriction (CMR) through which we can identify
transition dynamics based on observational data. We propose a provably
efficient IV-aided Value Iteration (IVVI) algorithm based on a primal-dual
reformulation of CMR. To the best of our knowledge, this is the first provably
efficient algorithm for instrument-aided offline RL.
