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The average return for trajectories, $V^{\pi_e}$(s) is often computed via the importance sampling estimate $$V^{\pi_e}(s) = \frac{1}{n}\sum_{i=1}^n\prod_{t=0}^{H}\frac{\pi_e(a_t | s_t)}{\pi_b(a_t|s_t)}G_i$$ where $G_i$is the reward observed for the $i$th trajectory. Sutton and Barton gives an example whereby the variance could be infinite. In general however, why does this estimator suffer from high variance ? Is it because $\pi_e(a_t|s_t)$ is mainly deterministic and therefore, the importance weight is 0 for most trajectories, rendering those sample trajectories useless ?

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