RL -- Value Fitting & Q-Learning

We can learn the value function and the Q-value function iteratively. In practice, we don't have enough memory for all the states. The most common method is to use a deep network as a function approximator. If the state space is continuous or large, it is not possible to use a large memory table to record V(S) for every state. However, like other deep learning methods, we can create a function estimator to approximate it.