Other replies are missing an explicit call-out to Reinforcement Learning. You can USE ML for RL, but the field itself is considered separate from ML and under AI in general.
From your link: “Due to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics.”
It all depends on whether you consider the new use as a particular application of a more general thing or as a thing on its own. (But I agree that if you call it with than name it’s not that general.)
The original comment was:
"So Artificial Intelligence is a superset of Machine Learning. What are some AI algorithms that are still in use, that is not Machine Learning"
It seems we agree with Wikipedia that ML contains RL?
It's true RL is also studied in other fields.
I struggle to see how that means that RL is a good answer to "What are some AI algorithms that are still in use, that is not Machine Learning".
Does ML contain game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics?
It may be the case if you define ML broadly enough. One may also define RL broadly to refer to things that existed well before ML was a thing (not that I would do it, but one may). I guess that may still be within the AI umbrella, but I’m not sure.
Reinforcement learning implies learning though. But of course that’s a term more appropriate in the context of “optimizing agents” than in the context of “optimizing prediction models”.
Reinforcement learning is a machine learning approach, there is no serious debate about that. The question is whether it is restricted to neural networks, or not.
For a bit of history on machine learning I recommend Rodney Brooks' seminal series of articles on machine learning, beginnign here:
The first article in the series, linked above has one section titled "Machine Learning Started with Games". In that section he goes over Arthur Samuel's checkers-playing program that beat a human champion in 1961.
The section also contains Brooks' description of Donald Michie's MENACE, which is widely considered to be one of the first reinforcement learning algorithms. For lack of a computer, it was implemented on a set of match boxes:
In 1960 Surgical Science did not have much pull in getting access to a digital computer. So Donald Michie himself built a machine that could learn to play the game of tic-tac-toe (Noughts and Crosses in British English) from 304 matchboxes, small rectangular boxes which were the containers for matches, and which had an outer cover and a sliding inner box to hold the matches. He put a label on one end of each of these sliding boxes, and carefully filled them with precise numbers of colored beads. With the help of a human operator, mindlessly following some simple rules, he had a machine that could not only play tic-tac-toe but could learn to get better at it.
I get what you mean. In principle things like policy iteration, value iteration and Q learning are not ML specific.
However, I didn't think of reinforcement learning when compiling that list, because in my experience non-ML RL solutions are rarely better than ML solutions. Happy to be corrected on that front.
Eg Wikipedia: "Reinforcement learning (RL) is an area of machine learning " https://en.m.wikipedia.org/wiki/Reinforcement_learning