This paper presents a physical model developed to find the directions of forces and moments required to open a plastic bag—which forces will contribute toward opening the knot and which forces will lock it further. The analysis is part of the implementation of a Q(lambda)-learning algorithm on a robot system. The learning task is to let a fixed-arm robot observe the position of a plastic bag located on a platform, grasp it, and learn how to shake out its contents in minimum time. The physical model proves that the learned optimal bag shaking policy is consistent with the physical model and shows that there were no subjective influences. Experimental results show that the learned policy actually converged to the best policy.