Temporal Difference Learning

In the previous chapter, Chapter 4, Gaming with Monte Carlo Methods, we learned about the interesting Monte Carlo method, which is used for solving the Markov Decision Process (MDP) when the model dynamics of the environment are not known in advance, unlike dynamic programming. We looked at the Monte Carlo prediction method, which is used for predicting value functions and control methods for further optimizing value functions. But there are some pitfalls with the Monte Carlo method. It is applied only for episodic tasks. If an episode is very long, then we have to wait a long time for computing value functions. So, we will use another interesting algorithm called temporal-difference (TD) learning, which is a model-free learning algorithm: it doesn't require the model dynamics to be known in advance and it can be applied for non-episodic tasks...