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by Stuart Armstrong 247 days ago | link | parent

It may generalize catastrophically to unseen situations (which is a key problem)

That probably summarises my whole objection ^_^



by Paul Christiano 247 days ago | link

But this could happen even if you train your agent using the “correct” reward function. And conversely, if we take as given an AI that can robustly maximize a given reward function, then it seems like my schemes don’t have this generalization problem.

So it seems like this isn’t a problem with the reward function, it’s just the general problem of doing robust/reliable ML. It seems like that can be cleanly factored out of the kind of reward engineering I’m discussing in the ALBA post. Does that seem right?

(It could certainly be the case that robust/reliable ML is the real meat of aligning model-free RL systems. Indeed, I think that’s a more common view in the ML community. Or, it could be the case that any ML system will fail to generalize in some catastrophic way, in which case the remedy is to make less use of learning.)

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by Stuart Armstrong 244 days ago | link

It seems like that can be cleanly factored out of the kind of reward engineering I’m discussing in the ALBA post. Does that seem right?

That doesn’t seem right to me. If there isn’t a problem with the reward function, then ALBA seems unnecessarily complicated. Conversely, if there is a problem, we might be able to use something like ALBA to try and fix it (this is why I was more positive about it in practice).

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