Abstract: A machine learning apparatus is configured to form an output value function for achieving an objective by iteratively performing: implementing a current state of the first agent function based on a current environmental state to form a subsequent environmental state and a first reward; a determining with the second agent function whether to use a second reward; if that determination has a negative outcome, refining the first agent function based on the first reward; and otherwise computing the second reward according to a predetermined reward function and refining the first agent function based on the first reward and the second reward; refining the second agent function based on a performance of the first agent function in meeting the objective; and adopting the subsequent environmental state as the current environmental state; and subsequently outputting the current state of the first agent function as the output value function.