Into which subcategories can reinforcement learning be divided? In the course of a scientific work, I will discuss the different types of reinforcement learning. However, I have difficulties to find these different types.
So, into which subcategories can reinforcement learning be divided? For example, the following subdivisions seem to be useful

Model-free and Model-based
Dynamic Programming, Monte Carlo and Temporal Difference

Any others?
Your two suggestions are not mutually exclusive. If you go by this process, you'll have to do a &quot;Cartesian product&quot; of a bunch of different RL categorizations which would get out of hand. I recommend, if you can, to describe some sort of &quot;RL taxonomy&quot; instead. By this I mean describing different RL characterizations without assuming they're mutually exclusive.
To add to your list :

On-policy or off-policy
Value based or policy gradient