Earlier quoted context omitted.
How many of those 500 actions are actually useful? I haven't watched competitive StarCraft games for years but back when I did, rates were more like 300APM and even then the players basically spam clicked the background or selected random units non-stop and were probably only doing 50-100 actual effective actions.
> How many of those 500 actions are actually useful? Exactly, a human doing 500 APM during intense moments is going to be way different than an AI bursting 1000 APM with pixel-precision during the most crucial moment in a game. TLO spent a ton of time at >1000 APM and walked his army directly into enemy shots all the time. MaNa had much better control at ~400 APM. So APM is really irrelevant to control - for humans.…
AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
191–200 of 459 posts
Re: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
#192Earlier quoted context omitted.
> How many of those 500 actions are actually useful? Exactly, a human doing 500 APM during intense moments is going to be way different than an AI bursting 1000 APM with pixel-precision during the most crucial moment in a game. TLO spent a ton of time at >1000 APM and walked his army directly into enemy shots all the time. MaNa had much better control at ~400 APM. So APM is really irrelevant to control - for humans.…
Such high actions per minute does not seem fun to me, and possibly a repetitive strain injury waiting to happen.
Re: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
#193Earlier quoted context omitted.
This is how it responds to cannon rush. : ) https://www.youtube.com/watch?v=vYdWQjTWTFM
This is not how a player would do a cannon rush, it needs to be hidden / at the edge of the opponent view.
Printf is part of a fairly small group of cannon rushers that don't simply see it as just another cheese, because what generally defines a cheese strat is that it can be easily countered if you know it's coming; not so with their cannon rushes.
Now, with that said, Printf (or any other "I always cannon rush" player aren't winning tournaments), but that's partly because not many players decide that they want to stake their development on any one strat like that, and if they do, it'll likely be one that's deemed more legitimate by the community.
Re: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
#194This is really impressive, I didn't expect starcraft to be played this well by a machine learning based AI. I'm excited to read the paper when it comes out! That said, I'm not sure I agree that it was winning mainly due to better decision making. For context, I've been ranked in the top 0.1% of players and beaten pros in Starcraft 2, and also work as a machine learning engineer. The stalker micro in particular looked…
> While they have similar APM to SC2 pros Wasn't the APM closer to half that of the pros? https://storage.googleapis.com/deepmind-live-cms/images/SCII...
Re: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
#195Earlier quoted context omitted.
Even the 200ms reaction time seemed overly slanted towards the AI. I don't think that is the actual reaction time of top pros, in the matches the AI played the human player would teleport in from complete invisibility and try to use an instant cast spell and the AI would have already teleported out. Yes the theoretically may have been constrained to a 200ms reaction time, but in practice the AI was playing at a super…
The game is difficult to watch, but does anyone honestly believe that an AI is going to have a difficult time parsing the scene if it is trained to do so? That to me just seems like a question of resources. We're pretty good at image recognition and segmentation now, and that's without the unlimited amounts of training data one could generate when using a controlled game environment with a limited range of possible a…
Re: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
#196Earlier quoted context omitted.
The results are obviously impressive, but even then there is a lot of work to do as far as learning efficiency goes: "The AlphaStar league was run for 14 days, using 16 TPUs for each agent. During training, each agent experienced up to 200 years of real-time StarCraft play. " MaNa probably played less than 2-3 years of Starcraft in his whole life (by that I mean 24hr x 365d x 3), and was learning with a much less foc…
Another way to think about it is that a human brain is mostly doing transfer-learning, on top of a 99%-baked deep net that was wired up during foetal development from our DNA, where that DNA-persisted model has "seen" hundreds of millions of years of training data. Humans don't have to learn to process, recognize, and classify objects in visual sense-data, for example. We can do that from the moment we're born, becau…
Re: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
#197Earlier quoted context omitted.
This is exactly what I think, I'd like to see how Alphastar react to "cannon rush" or other weird bo where you need to be "smart" to counter it and just not be based on insane / none human micro.
AlphaStar makes up for its slightly subpar macro with REALLY good at micro. Thus, more micro heavy counters like cheeses are unlikely to beat it.
Re: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
#198Does anyone know if this opens the door to using these techniques for poker (given that they've now show success on games of imperfect information)? Thus far the solutions to poker have involved solving the game tree through raw computational power and clever methods of information collapsing: http://science.sciencemag.org/content/347/6218/145 But it seems the techniques used here might be both far more efficient, as…
And to add that, what if deepmind could get a poker history of the players at the table to create a poker profile of each player it is playing against. Having a percentage of a player's likelihood of folding and bluffing could keep it an advantage over a purely objective game theory aspect of the game. Maybe a certain player is more likely to bluff 5 hours into a game based off of player history analysis. Going furth…
If you have player history you can construct perfect models for their playing habits and even update it as time goes on like a multi armed bandit problem. This can be simple probability heuristics and an optimization function.
The problem with it is that the machine becomes biased to past results. Top human players are good because they adapt. I would put my money on the humans
Re: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
#199This is really impressive, I didn't expect starcraft to be played this well by a machine learning based AI. I'm excited to read the paper when it comes out! That said, I'm not sure I agree that it was winning mainly due to better decision making. For context, I've been ranked in the top 0.1% of players and beaten pros in Starcraft 2, and also work as a machine learning engineer. The stalker micro in particular looked…
Re: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
#200Earlier quoted context omitted.
> How many of those 500 actions are actually useful? Exactly, a human doing 500 APM during intense moments is going to be way different than an AI bursting 1000 APM with pixel-precision during the most crucial moment in a game. TLO spent a ton of time at >1000 APM and walked his army directly into enemy shots all the time. MaNa had much better control at ~400 APM. So APM is really irrelevant to control - for humans.…
Such high actions per minute does not seem fun to me, and possibly a repetitive strain injury waiting to happen.