It seems like we're going back to expert systems in a kind of inverted sense with all of this chaining of deterministic steps. But now the "experts" are specialized and well-defined actions available to something smart enough to compose them to create new, more powerful actions. We've moved the determinism to the right spot, maybe? Just a half-thought.

I'm just trying to learn this stuff now, so I don't the literature. The "trajectory view" through action space is what makes the most sense to me.

Along these lines, another half-baked pattern I see is kind of a time-lagged translation of stuff from modern stat mech to deep learning/"AI". First it was energy based systems and the complex energy landscape view, a-la spin glasses and boltzmann machines. The "equilibrium" state-space view, concerned with memory and pattern storage/retrieval. Hinton, amit, hopfield, mackay and co.

Now, the trajectory view that started in the 90s with jarzynski and crooks and really bloomed in 2010+ with "stochastic thermodynamics" seems to be a useful lens. The agent stuff is very "nonequilibrium"/ "active"-system coded, in the thermo sense... With the ability to create, modify, and exploit resources (tools/memory) on the fly, there's deep history and path dependence. I see ideas from recent wolpert and co.(Susanne still, crooks again, etc.) w.r.t. thermodynamics of computation providing a kind of through line, all trajectory based. That's all very vague I know, but I recently read the COALA paper and was very enchanted and have been trying to combine what I actually know with this new foreign agent stuff.

It's also very interesting to me how the Italian stat mech school, the parisi family, have continuously put out bangers trying to actually explain machine learning and deep learning success.

I'd love to hear if anyone is thinking along similar lines, or thinks I'm way off track, has paper recs please let me know! Especially papers on the trajectory view of agents.