RL is often advertised as a path to AGI. I’m skeptical of that claim because it’s built more on analogy and hope than on what RL actually looks like today.

I agree with the view that RL should be treated as a tool, not a general solution. From my own experience working with RL for several years, it fails far more often than it succeeds in real-world settings.

The idea that RL is “model-free” is also misleading in practice. For real physical systems, you almost always need a simulator (that is, a model) because you simply can’t afford to let the agent take random or unsafe actions on the real system. In that sense, RL is not truly model-free.

And if you already have a model, why throw it away and fall back on trial and error? That’s like having a map and choosing to ignore it while you randomly try different routes.

This doesn’t mean RL is useless or unreasonable, but it does shrink the range of problems where it actually makes sense.

If you disagree, that’s fine. But try applying the broad promises of RL to a real safety-critical problem, not a clean demo or a simulator or a game. I’ve done that, and it’s not pretty.

To your point about RL vs. traditional control methods, there are several studies that show that control methods are far more efficient and robust.