Edit 2: Reading through the "supplementary data" of the 2019 paper, it definitely appears that the AlphaStar which reached grandmaster was not limited in the same ways as the 2017 paper would suggest. x/y positions of units are not determined visually, but fed directly from the API. So AlphaStar absolutely can just run Attack(Position: carrier_of_interest->pos.x) and not mis-click. It's "map" / "vision" is really just a bounding box of an array of every entity/unit on the map and all the things that a human would have to spend APM to manually check (precise energy level, precise hit points remaining, exact location of invisible units, etc). See [7]. DeepMind showed they have some fixed time delays to emulate human experience, if they had a position fuzzer, they would have mentioned it. I'm reasonably convinced they gave AlphaStar huge advantages even in the 2019 version that was 'nerfed' from the 2018 version. The 2017 paper was a more ambitious project IMO that didn't quite get fully developed.

Edit: 7 minutes after writing this I re-read the original paper[-1]. https://arxiv.org/pdf/1708.04782.pdf page 6 and 7 make it clear that DeepMind limited themselves to SpatialActions, so they cannot tell units "Attack Carrier" but have to say "Attack point x,y" (and x,y also has to be determined visually, not through carrier_of_interest->pos.x ). It's still not clear in the paper if any randomness is added to Attack(x,y).

Additionally, I have some serious concerns about assuming that the design decisions made in this 2017 paper were actually used in the implementation of the 2019 Alphastar demo vs TLO and MaNa. The paper claims "In all our RL experiments, we act every 8 game frames, equivalent to about 180 APM, which is a reasonable choice for intermediate players." I would agree with this choice! But [5][6] indicates that Alphastar's APM spiked to over 1500 APM in 2019! And even in moments when a human reaches that APM, their EPM would be an order or magnitude lower, whereas Alphastar's EPM matches its APM.

Original post:

Thank you so, so much for adding to the discussion! Would love to chat more about this if you see my reply and feel like it.

Regarding "mis-clicking", my understanding was that AlphaStar used Deepmind's PySC2[0][1], which in turn exposes Blizzard's SC2 API[2][3].

Here is the example for how to tell an SCV to build a supply depot:

Actions()->UnitCommand(
unit_to_build,
ability_type_for_structure,
Point2D(
unit_to_build->pos.x + rx * 15.0f,
unit_to_build->pos.y + ry * 15.0f
)
);

where unit_to_build->pos.x and unit_to_build->pos.x are the current position of the SCV and rx and ry are offsets. It's possible to fuzz this with some randomness, and indeed in the example, rx and ry are actually random (because the toy example just wants to create a supply depot in a truly random nearby spot, it doesn't care where). But the API doesn't attempt to "click" on an SCV and then use a hotkey and then "click" somewhere else. The API will never fail to select the correct SCV. It will also build precisely at the coordinates provided.

Point 1: Even if DeepMind added a fuzz to this method to make it so AlphaStar can "misclick" where the depot gets built, it cannot accidentally select the wrong SCV to build that depot. (Possibly wrong, as they could be using SpatialActions, see below)

Point 2: Most bot-makers wouldn't add a random fuzz to the depot placement coordinates to make their AI worse and I'd be super surprised if there was hard evidence somewhere that Alphastar had such a fuzz. (This is my main concern.)

My personal conclusion was that anything which looks like a "misclick" is, in fact, a "mis-decision". A human can decide "I want my marines to attack that carrier" but accidentally click the attack onto a nearby interceptor. I didn't think Alphastar could do that because I assumed it would use the Attack(Target: Unit) method instead of Attack(Target: Point) in that scenario -- and even if they used Attack(Target: Point) it would be used as Attack(Target: carrier->pos.x).

However, I realize now that they could be doing everything with SpatialActions (edit: it does, see paper[-1] pp. 6-7) (select point, select rect's)[4], and that they could have a implemented a randomness layer to make alpha star literally mis-click.

I suppose I would need to test this API and dive into the replay files to first see if its possible to discern the different between Attack(Target: carrier_of_interest) and Attack(Target: carrier_of_interest->pos.x). Then, even if Alphastar is using the latter, it's still not clear that there's an additional element of randomness outside of the AI/ML control.

Has anyone already done an analysis of the replay files on this level, or has DeepMind released hard info on how they're controlling the bot?

-1: https://arxiv.org/pdf/1708.04782.pdf

0: https://www.youtube.com/watch?v=-fKUyT14G-8

1: https://github.com/deepmind/pysc2

2: https://github.com/Blizzard/s2client-proto

3: https://blizzard.github.io/s2client-api/index.html

4: https://blizzard.github.io/s2client-api/structsc2_1_1_spatia...

5: https://www.alexirpan.com/2019/02/22/alphastar.html

6: https://deepmind.com/blog/article/alphastar-mastering-real-t...

7: https://ychai.uk/notes/2019/07/21/RL/DRL/Decipher-AlphaStar-...