  Robot control policies learned in simulation do not often transfer well to
the real world. Many existing solutions to this sim-to-real problem, such as
the Grounded Action Transformation (GAT) algorithm, seek to correct for or
ground these differences by matching the simulator to the real world. However,
the efficacy of these approaches is limited if they do not explicitly account
for stochasticity in the target environment. In this work, we analyze the
problems associated with grounding a deterministic simulator in a stochastic
real world environment, and we present examples where GAT fails to transfer a
good policy due to stochastic transitions in the target domain. In response, we
introduce the Stochastic Grounded Action Transformation(SGAT) algorithm,which
models this stochasticity when grounding the simulator. We find experimentally
for both simulated and physical target domains that SGAT can find policies that
are robust to stochasticity in the target domain
