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#play#starcraft#https#games#bot#more#agent#com#played#zerg
Discussion Sentiment
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Discussion (62 Comments)Read Original on HackerNews
I love StarCraft. I started playing it right from the beginning, most of my friends right now are from that era. I literally met people that have spread to almost every continent when I was in my early teens. We played at internet cafes and did not have access to the internet, that was priced differently...
I miss those days so much.
Everybody was from a different background back then, and nobody was anything other than a guy that plays StaCraft at the cybercafe... And now, we are in our 40's and I know Math teachers, history teachers, oil rig operators, software programmers, professional gamers, lawyers and more... hahah So crazy to think about it... and I know them, we talk, what a world.
My first time playing StarCraft was at summer camp around a decade after it came out.
All the smartest people played it so I wanted to too. Great decision, I have been continually impressed with the people who StarCraft introduced me to.
Protoss: powerful and expensive frontier coding agents you directly micromanage for the toughest tasks
Terran: versatile team comps of dedicated agent roles you can delegate well-defined tasks to
Zerg: massive swarms of specialist custom agents inside your apps that you evolve and optimise for speed and cost
Knowing every faction has its strengths and weaknesses helps me decide which tools to use for the job.
https://web.archive.org/web/20091124210529/http://eis.ucsc.e...
There's a great contemporary Ars Technica piece by a competitor:
https://arstechnica.com/gaming/2011/01/skynet-meets-the-swar...
As an undergrad I did a project using genetic programming. It was not very successful, but it was a lot of fun.
https://tomisin.space/archive/starcraft-genetic-programming/
I've watched several replays on this. It using map hacks is lame but it also doesn't always take advantage of them. It sometimes does respect its own fog of war. It mostly wins with incredible micro (kinda has to, it's macro kinda sucks).
Most of its few losses come from drops (it never makes turrets and doesn't know how to handle them), bad macro (blocking its own ramp) or just incredibly unconventional play from its opponent (which is certainly not in its training set).
[1] https://rolandgao.com/blog/gobench/
[2] https://rolandgao.com/gobench.pdf
Programming an engine OTOH is a skill that is more general and they should all have.
Might be useful to have the target of the engine be some specific virtual machine that gets a strict cycle budget-- e.g. execution runs so many cycles, and result is read out of a specific memory address at the end (or when it terminates early).
And it's also just bencmaxxing bait: you can get a huge improvement on the task by RLing on it, but make no improvement on anything else. Doing so would just waste model capacity.
If you could tell that every LLM was equally not being exposed to the task then you could justify it as a test of abstract reasoning, but you can't. So it ends up on how much go transcripts ended up in the training, which is ... not a very interesting metric.
It sounds like it might have been actually played in real time, which would be very important to distinguish.
I have recently seen other harnesses letting agents play real-time games in what seems like discrete time slices, turning eg Portal into something turn-based https://www.youtube.com/watch?v=ruuGXFAmiOE
Must recently I built out a MMORPG puzzle box thing, I wrote a general game architecture doc but left the specific puzzle design up to Fable. Nobody is actively playing rn but I left it up at bot.willmorrison.net.
For agent harness I did Claude Code, Codex, Grok Build. This was primarily a cost driven decision — I have a lot of free tokens and I didn't want to pay API prices for this.
For game harness I used minimal BW-API issue command and get observation apis as tools. I felt this was the most fair way to do it on my small scale.
In the future I would like to integrate code mode and multiple games/I think if it was a best of 5 where each agent could learn from its past games and build its own automations over time that would be much more interesting.
If you could win as Protoss by building Dark Archons, raiding your Zerg opponent's base to capture a worker, building a bunch of Zerg production and tech buildings, and attacking with a combined army... you most likely outclass your opponent, and could have won much faster using a more conventional strategy.
AlphaStar won a showmatch against TLO. TLO was never one of the strongest players in the world. He had been retired for over three years by the time of the match. Google set the rule that their system would have human-like mechanics, but it played several times faster than any human, never issued a wasted action, had an inhumanly fast reaction time, issued commands with perfect accuracy using an API, and could see the entire map at once.
It was later released to the open ladder with more human-level mechanics. Even strong amateurs regularly beat it. I have beaten it myself. It was strong, but not even close to the level of the strongest human players. It had obvious and easily-exploitable deficiencies in strategy and building placement.
I think even the cheater version would have lost handily to Serral or any of the strongest players.
(It apparently beat MaNa as well as TLO, but those matches were never released to my knowledge. I see no reason to assume Google cheated less flagrantly in private than they did in public.)