# How can deep Q-learning converge if the targets may not be correct?

In deep Q-learning, $$Q(s, a)$$ and $$Q'(s, a)$$ are predicted or estimated by the neural network itself. In supervised learning, the target value is a true unbiased value. However, this isn't the case in reinforcement learning. So, how can we be sure that deep Q-learning converges? How do we know that the target Q values are accurate?

• – nbro Aug 3 '20 at 14:45