One Q update

Q-learning nudges one state-action value toward a target. The target is the reward plus gamma times the best Q in the next state. The step size alpha says how far to move from the old Q toward that target.

Q starts at 0. The reward is 2, gamma is 0, and alpha is 0.5. Enter the Q after this one update.