 The Q value for a state-action is updated by an error, adjusted by the learning rate alpha.Q values represent the possible reward received in the next time step for taking action "a" in state "s", plus the discounted future reward received from the next state-action observation.Watkin's Q-learning updates an estimate of the optimal state-action value function formula_2 based on the maximum reward of available actions.