Reinforcement Learning Algorithms with Python

Function approximation

Now that we have talked about the main constraints of tabular algorithms and expressed the need for generalization capabilities in RL algorithms, we have to deal with the tools that allow us to get rid of these tabular constraints and address the generalization problem.

We can now dismiss tables and represent value functions with a function approximator. Function approximation allows us to represent value functions in a constraint domain using only a fixed amount of memory. Resource allocation is only dependent on the function that's used to approximate the problem. The choice of function approximator is, as always, task-dependent. Examples of function approximation are linear functions, decision trees, nearest neighbor ...