I would like to know a list of model-based and model-free reinforcement learning algorithms, like Q-learning, SARSA, TD, Dyna-Q.
The surprising fact is, that model-free algorithm like q-learning can be used for model-free and model-based learning as well. The forward model can be stored in the q-matrix and it can be modified by changing the parameters. What we can say in general is, that model-free algorithms are discussed very often, and model-based learning is some kind of non-conformist idea. The combination of reinforcement learning plus model-based control is a promising technology which will allow to solve complex domains. Sometimes, the principle is called sample-efficient because the algorithm doesn't contain trial-by-error but is working with a plan.
It is a bit complicated to separate between learning algorithm for model-based and model-free assumption. What we can say in general is, that the idea is to learn the parameters. Which means it has to do with raw-data stored in a database. And build a model, a policy and a reward function on top of the data.
Q-learning is often described as a model-free technique. But it is only a special case. It can be model-based as well. The forward model is described as a markov decision process. The transition from one state to another of the system are given in the q-table. In case of online system identification, It's called bayesian q-learning.