Can anyone shed light on in what way this is more challenging than the starcraft or dota agents, which also had to work with imperfect information?
Stratego is 40% information, 40% strategy, maybe 10% tactics. If you know where is the flag it's trivial to win in almost all situations. Fighting into imperfect information is literally all the game.
Like is it just the speed of your clicking? Or is it more than that, like the most basic kinds of strategic decisons?
What's your MMR, out of curiosity?
I do think OpenAI five derived some of their advantage from seamless ability usage, and inhuman coordination in lane, but it also did some novel things strategically, that challenged some of the established tenets of high level play (e.g. they had a lot of mobility on the map, back when that was considered very inefficient).
It's obviously not about clicking fast, but it is about timing, sometimes 100 milliseconds reaction time make huge difference in outcome. It is usually making decisions on very small time scales. Do you retreat or continue? Use ability or hold it? Can you overextend?
The only meaningful strategic decisions in dota (which you have long time frame of deciding and effect the game for a long duration) are draft (which AI doesn't really master, they reduced the heroes pool to simplify) and item purchases, and there are only a handful of them (~6) in an entire game. Other decisions don't really have a long "memory" time, a minute or two at the most. After two minutes every other decision is just reduced to the relative advantage between the teams.
There used to be one hero in Dota which made it a strategy game instead (techies). But it was like playing a different game and everyone hated it and it was effectively removed. Techies was like playing stratego against chess players, they obviously get pissed off by not playing what they wanted.
(The old joke is that Dota is a 1 v 9 game, not a 5 v 5)
Both modes of play are fun, mind, but the parallels struck me as worth noting.
Stratego is way more challenging than Poker, though. StarCraft/Dota/Stratego have the property that you can’t represent their imperfect information as a vector in memory, whereas you can easily do that in Texas hold’em poker (there’s only 52C2 = 1024 possible hands). So for those games, you have to use an approximate distribution rather than the exact one.
I’m an author on this paper (although my contributions were relatively minimal) and on the Player of Games paper (which did Poker, Chess, and Go).
My very naive question question being: Why tackle this after SC and Dota have already been done? What is the scientific interest? Stratego seems strictly simpler than both of these games. In what way is this an advancement over how SC/Dota AI were solved?
In principle, the AlphaStar's league approach (from StarCraft) could be done also in Stratego, and it would be very interesting to compare the two approaches. Note that AlphaStar is more expensive: it required to train N competing agents with pair-wise evaluation costing N^2, while Stratego's NeuRD trains a single agent.