is hit very far away and can not recover. The first row shows what percentage of the 500 different tasks failed. The mean error is the average error of the remaining tasks that did not fail. The error is calculated as the mean position error over time. Thus, it is a crude measure of both how fast the controller gets to the goal and how well it stays on it.[]{data-label="table:model_errs"}

![**Action constraints**. The same learned model is used in two different controllers with different constraints on how large the action can be. The solid line is the trajectory of a controller with a lower action magnitudes, while the dotted line is the trajectory of a controller with higher action magnitudes. The ball can be controlled to bounced in a stable fashion for each set of constraints.[]{data-label="fig:speed_constraints"}](images/speed_constraints.png){width="40.00000%"}

![**State constraints**. The same learned model is used in two different controllers with different constraints on the state space. The orange controller has no state constraints, while the teal controller has a constraint that prevents the ball from bouncing within the blue circle. The paths of both controllers are plotted and the dots along the path represent points where the ball bounces on the 