That’s one possible solution, but it only works to the extent that your simulator accurately reflects reality. Any hard coded simulation is therefore doomed to fail badly, and any adaptable “simulation” isn’t really a simulation at all — just more neural architecture.

> So we need simulation both outside and inside agents.

We don’t even need a simulation outside of agents, if you just use physical robot bodies :) Humans do just fine learning without extra simulation environments — the real world is enough.

Of course, simulated environments are incredibly useful, but not in the ways you’re claiming. Mostly it’s because reinforcement learning is still so slow to learn that without a simulated environment it would take forever to test anything. Also, they allow repeatable, testable results that allow learning effectiveness to be measured scientifically, etc.

Also I know you can formulate RL such that you use a simulator as part of the learning algorithm, but IMO that is cheating at worse, and a misguided rewsearch path at best.

> Neural nets by themselves are not even the complete answer.

Only to the extent that human brains are also not the complete answer to intelligence (i.e. maybe there’s a possible brain architecutre much better than ours), but I don’t think that’s what you’re saying.

If you meant the current wave of artificial neurons are not the complete answer, then of course I agree. But I don’t think that’s what you’re saying either.

Show me the part of our brain that you think contains a perfect simulation of reality and I’ll concede. Now certainly, our brain can do a kind of “neural simulations” to understand and reason about consequences of actions, but that “simulation” (if we can even call it such) is learned.