  Motivated by the many real-world applications of reinforcement learning (RL)
that require safe-policy iterations, we consider the problem of off-policy
evaluation (OPE) -- the problem of evaluating a new policy using the historical
data obtained by different behavior policies -- under the model of
nonstationary episodic Markov Decision Processes (MDP) with a long horizon and
a large action space. Existing importance sampling (IS) methods often suffer
from large variance that depends exponentially on the RL horizon $H$. To solve
this problem, we consider a marginalized importance sampling (MIS) estimator
that recursively estimates the state marginal distribution for the target
policy at every step. MIS achieves a mean-squared error of $$ \frac{1}{n}
\sum\nolimits_{t=1}^H\mathbb{E}_{\mu}\left[\frac{d_t^\pi(s_t)^2}{d_t^\mu(s_t)^2}
\mathrm{Var}_{\mu}\left[\frac{\pi_t(a_t|s_t)}{\mu_t(a_t|s_t)}\big(
V_{t+1}^\pi(s_{t+1}) + r_t\big) \middle| s_t\right]\right] +
\tilde{O}(n^{-1.5}) $$ where $\mu$ and $\pi$ are the logging and target
policies, $d_t^{\mu}(s_t)$ and $d_t^{\pi}(s_t)$ are the marginal distribution
of the state at $t$th step, $H$ is the horizon, $n$ is the sample size and
$V_{t+1}^\pi$ is the value function of the MDP under $\pi$. The result matches
the Cramer-Rao lower bound in \citet{jiang2016doubly} up to a multiplicative
factor of $H$. To the best of our knowledge, this is the first OPE estimation
error bound with a polynomial dependence on $H$. Besides theory, we show
empirical superiority of our method in time-varying, partially observable, and
long-horizon RL environments.

Machine Learning;Artificial Intelligence;Machine Learning