 Value iteration can also be used as a starting point, giving rise to the Q-learning algorithm and its many variants.The problem with using action-values is that they may need highly precise estimates of the competing action values that can be hard to obtain when the returns are noisy, though this problem is mitigated to some extent by temporal difference methods.Using the so-called compatible function approximation method compromises generality and efficiency.