  This paper is concerned with the problem of designing agents able to
dynamically select information from multiple data sources in order to tackle
tasks that involve tracking a target behavior while optimizing a reward. We
formulate this problem as a data-driven optimal control problem with integer
decision variables and give an explicit expression for its solution. The
solution determines how (and when) the data from the sources should be used by
the agent. We also formalize a notion of agent's regret and, by relaxing the
problem, give a regret upper bound. Simulations complement the results.
