Honestly, good old classification losses without RL would probably be fine. And there are already a billion datasets for that.
The exception is when you want it to be more of an agent where there are sequential dependencies. Then you probably need RL for that kind of competency.
![Quote image of wikipedia history saying: "The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.[6][7] The synonym self-teaching computers was also used during this time period.[8][9]"](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:74fhcdv7jj53pu254mlql3kr/bafkreihnhoqu553dpunf5qxr7bfsgsvatmidwlxm35l76aprhca765b5yu)
![Quote image of wikipedia ML section on relationship to AI. It includes an image of ML circle being contained in the AI cirlce. It also says: "As a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalised linear models of statistics.[26] Probabilistic reasoning was also employed, especially in automated medical diagnosis.[27]: 488 "](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:74fhcdv7jj53pu254mlql3kr/bafkreicoxi7hfwp3y4wzpbncrofoavpn2islclthnz2kfkmrub2vf2hnqu)
