Dr. Jeff Beck

We're specifying things that are all related to how it is we compute our policies.

They're latent variables that represent policies.

that are compatible with reinforcement learning.

And that's the defining characteristic of an agent.

But you could very easily just say from an outside perspective, if you can't look at how someone or something is doing the computations, if the only thing you observe is the policy,

Does that mean that you can never conclude that something's an agent?

And I would say no.

You'd still like to be able to conclude that this is an agent, even though the only thing I ever get to measure is its policy.

Is this like a measure of agency?

Is that what you mean?

Yes.

So, I mean, I think you could use notions of transfer entropy and things like that in order to estimate the timetable for which something is incorporating information or the degree to which it exhibits a context-dependent behavior and things like that.

And that would be a pretty good measure.

Now, is it normative?

No, it's not.

But it is a measure and you could use things like that.

But at that point, you're really just talking, again, about policy sophistication.

Right.

Not does it have a reward function?

Like, is it actually executing planning?