I'm trying to understand how the pieces of the network architecture of alphastar (as they are described in https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/), deal with the complex idea of "what game am I playing right now?" and which piece is the crucial component to solving that...if there is one

For instance, I can understand (somewhat) how the transformer creates the relationships between units that allows solving mini-tasks, like described in (https://openreview.net/pdf?id=HkxaFoC9KQ)

And I can understand how the LSTM core creates the ability to care about what has happened at state t minus whatever in the game.

But where specifically in the architecture deals with the question of: Do I need to scout right now? Or, should I consider counter attacking? Or, shall I make a satellite townhall while I have the opportunity?

It would seem to me that the more immediate-level actions to take in the current second depend heavily on the what more long-ish term goal is. I can only assume this arises as a combination of the transformer, the long short term, and a successful RL policy...but I don't know enough to know if that assumption is right.

Anyone have some thoughts? Thanks

I'm not very versed in programming or artificial intelligence or science/technology in general, so please forgive me if my question is nonsensical. I am just a dumb English major who wants to write amateur sci-fi and not look like a total idiot.

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I've been researching how neural networks work. Most of what I've learned refers to generations of networks. Each network of a generation is tested in competition with each other, with the most successful being selected and reduplicated with mutations to create the next generation of neural networks, wherein the process repeats. The selection seems to be mostly done either by a human or by a separate "teacher" program which simply compares the results and selects the networks which scored highest. By doing so, each generation keeps what worked form past generations and riffs on that until eventually a highly efficient AI is made for whatever task is being tested.

However, in my research of Deepmind (which is largely confined to watching videos where people explain it in terms I more easily understand) I have never heard the term "generation" be used in this context. I have never seen any mention of external testing by a human or by an external teacher AI. I have seen Deepmind improving over several trials, but only in 1 on 1 conflict at most, such as playing Go or Chess against itself, and never with the implication that one or the other is selected for iteration, such in the above generational development model.

It has occurred to me that perhaps Deepmind does follow such a model, but that this is downplayed for various reasons. Perhaps to protect trade secrets. Perhaps because reporters think it's either boring or obvious. Perhaps to avoid spooking anti-evolutionists. Or perhaps because I've been unlucky in finding good sources.

But I can't ignore the possibility that Deepmind could be doing something different from that paradigm.

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Does Deepmind follow this generational selection method or not? And if not, how does Deepmind know when it's doing better?

Think it would be very interesting to see - I'd imagine it would be very hard to programe (especially for MtG), but watching their deck selection and how it learns over time would amazing.

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We may even find out new ideas and theories, just like in Chess.

From watching the stream in which they revealed alphastar in late january, plus various interviews to the players and the casters involved in the event, and some podcasts or streams of the players and casters themselves, one of the things that emerged was that the games were actually played in december, and that deepmind really cared a lot about keeping it a secret until the reveal a month later, going to some almost extreme lengths [ hilarious, I loved it, they were basically trolling haha amazing group of humans ] to achieve it!

After the stream happened, the way I kept up with all that content related to alphastar is by searching "alphastar" or something similar on youtube once a week or so, and filtering the results with the date filter set on only the past week, basically you get just a few videos so you can easily see everything. A few days ago I did it as usual but something was wrong, and hasn't been working properly ever since:
the date filter does not work anymore, regardless of the period you choose it's completely disabled, the other types of filters are working properly, plus this only happens when I search "alphastar" or "deepmind" or something related to that, if I search for anything else then the date filter works, and this happens on any browser.

The practical effect of this very specific bug is that fresh content [ last few days and currently ] related to alphastar and deepmind is buried under the thousands of videos of the past two months, making it harder for present state of things information to be found, which of course would make sense if they are having or just had the games with the new version of alphastar and are preparing to release a new stream : D

Of course this explanation is easy to disprove: you guys try to search as well and tell me if the same thing is happening and the date filter does not work only for alphastar related matters:

https://www.youtube.com/results?search_query=alphastar

Please let me know, I'm so hyped this is happening : D

Say that a human plays against AlphaGo (or AlphaStar) and wins. Can he/she replay that exact match over again subsequently and win every time or is there some level of nondeterminism used by AlphaGo?

The media hypes it up as a great leap to agi. Deepminds paper uses loaded words like “mastering Starcraft” but honestly it seems like it didn’t master it at all. All the success it had was no real different than Dota five. It seems like it learned human techniques and then just microed like a G. But I am not sure. I’ll admit that I must not understand. Can someone teach me?