Product Distribution (PD) theory is a new framework for controlling Multi-Agent Systems (MAS?s). First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. Accordingly we can consider a team game havbing a shared utility which is a performance measure of the behavior of the MAS. For such a scenario the game is at equilibrium — the Lagrangian is optimized — when the joint distribution of the agents optimizes the system?s expected performance. One common way to find that equilibrium is to have each agent run a reinforcement learning algorithm. Here we investigate the alternative of exploiting PD theory to run gradient descent on the Lagrangian. We present computer experiments validating some of the predictions of PD theory for how best to do that gradient descent. We also demonstrate how PD theory can improve performance even when we are not allowed to rerun the MAS from different initial conditions, a requirement implicit in some previous work.