We investigate the problem of extracting a control policy from a single or
multiple partial observation sequences. Therefore we cast the problem as a
Controlled Hidden Markov Model. We then sketch two information-theoretic
approaches to extract a policy which we refer to as A Posterior Control
Distributions. The performance of both methods is investigated and compared
empirically on a linear tracking problem
