Planning enables autonomous agents to solve complex decision-making problems by evaluating predictions of the future. However, classical planning algorithms often become infeasible in real-world settings where state spaces are high-dimensional andtransitiondynamicsunknown.

Weexploit suchprototypes forboththe clustering and retrieval of unlabelled cardiac signals based on multiple patient attributes.

Empirical risk minimization (ERM) is a crucial framework that offers a general approach to handling a broad range of machine learning tasks.

NaiveIS estimator involves products of the form π(at | xt)/µ(at | xt) and is infeasible in practice due to high variance. To control the variance, a line of prior work has focused on operator-based estimation to avoid fullIS products, which reduces the estimation procedure into repeated iterations of off-policyevaluation operators [1-3].