EPTAlphaStar AI goes 10-1 against human pros in demonstration…
| Forum Index > SC2 General |
|
Wormer
Russian Federation77 Posts
| ||
|
papaz
Sweden4149 Posts
I think the main problem with mainstream audience is that their expectations from the first iteration is off the charts + they have little understanding on the concept of "AI" which is an unfortunate term used by mainly movies and then by media to draw attention. Anyway, great work by the Deepmind team. Truly impressive to see this development being done in so short amount of time. I honestly wasn't expecting this so soon. Here is hoping that the AI will participate in GSL in 2 years, AlphaStar fighting!! | ||
|
deacon.frost
Czech Republic12129 Posts
On January 29 2019 15:43 Excalibur_Z wrote: Good point. Just to add to this a bit: while it is certainly true that medium- and large-scale battles are visually hard to follow and difficult to "score" as the fast action unfurls for casters, another major reason they often won't know who's going to win a fight is because the unit control from each player is also highly variable. Some players have different unit targeting priority tendencies and that can influence outcomes, some are better at positioning, some can more effectively exploit their favorite units, some click faster, some click more accurately, some react faster, and so on. AlphaStar chooses to take an engagement against an enemy force knowing that it will have near-100% unit efficiency and assuming that the enemy will have the same (since it has prior experience playing against itself). Human play can only introduce mistakes, resulting in relative inefficiency. There was only one moment that I saw where AlphaStar made a critical blunder in a skirmish, and that was in the Blink Stalker game (Game 4 vs MaNa) where it blinked forward and lost like 7 Stalkers in exchange for 1 Immortal. By the odds, if AlphaStar pushes forward against you, it's doing so with extremely high confidence backed up by centuries of experience. Even MaNa himself said on the Youtube video in the other thread that the AI just played without the fear and that's what made the big difference. e.g. the stalker rush. AlphaStar just went up and killed MaNa because it either knew MaNa doesn't have the sentry to force field ramp or it knew the fight will be benefitial to it even with the sentry! What human player would do that without a blink? honestly? Who? Not even sOs or Has are that crazy. Video (in case it was this thread I saw it in I'm sorry for reposting  )Some time-marks Stalker rush - 0h20m Immortal rush - 1h01m, at 1:03 MaNa talks how ballsy AlphaStar goes through the ramp I don't remember the time mark when MaNa talks about the agents not having any fear. Also MaNa talks how the immortal drop did NOT win him the show match  And no offense to anyone in this thread, but I will believe more MaNa than you  Edit> This is not against you, Excalibur_Z, it just happens I replied to your post, the end is my general rant | ||
|
MadMod
Norway4 Posts
The best part was the Stalker battle engagement evaluation. Most "reactions" it did was very adapted to a bizarro bot game universe with bots overfitted to that environment. There were also the hilarious total failures, especially obvious in game 4 and the exhibition match. - The failure to build a new robo or cannons after losing it to 3 DTs and getting only one observer out. - The attacking of its own forge. - The exhibition match hilarious ending. - The total lack of forcefield understanding. If each agent played many matches, and especially longer matches, this would have become much more obvious I think. There is also the complete lack of scouting, which is probably since scouting is a negative/loss in the short term, but good in the long term only if you react to it. This local minima is probably very hard to escape. I am not sure how they plan to deal with this problem in their learning. Even with the insane micro I think MaNa goes 100-0 after training against it a bit. He just needed to unlearn to engage in razors edge micro battles. | ||
|
maybenexttime
Poland5700 Posts
On January 29 2019 15:43 Excalibur_Z wrote: Good point. Just to add to this a bit: while it is certainly true that medium- and large-scale battles are visually hard to follow and difficult to "score" as the fast action unfurls for casters, another major reason they often won't know who's going to win a fight is because the unit control from each player is also highly variable. Some players have different unit targeting priority tendencies and that can influence outcomes, some are better at positioning, some can more effectively exploit their favorite units, some click faster, some click more accurately, some react faster, and so on. AlphaStar chooses to take an engagement against an enemy force knowing that it will have near-100% unit efficiency and assuming that the enemy will have the same (since it has prior experience playing against itself). Human play can only introduce mistakes, resulting in relative inefficiency. There was only one moment that I saw where AlphaStar made a critical blunder in a skirmish, and that was in the Blink Stalker game (Game 4 vs MaNa) where it blinked forward and lost like 7 Stalkers in exchange for 1 Immortal. By the odds, if AlphaStar pushes forward against you, it's doing so with extremely high confidence backed up by centuries of experience. I would strongly disagree with Athinira's claim. There is no evidence that AlphaStar perfectly predicted the outcomes of the battle. At best, it thought it had the upper hand in those engagements under the assumption that its opponent was equally mechanically capable. MaNa obviously is not, which made those engagements so decisive. In order to verify how reliable its assessment of the engagements is you'd have to analyze the AlphaStar league games. edit: grammar | ||
|
KelsierSC
United Kingdom10443 Posts
On January 29 2019 18:36 papaz wrote: ITT lot of people who don't see nor understand how impressive this is + human feelings hurt. I think the main problem with mainstream audience is that their expectations from the first iteration is off the charts + they have little understanding on the concept of "AI" which is an unfortunate term used by mainly movies and then by media to draw attention. Anyway, great work by the Deepmind team. Truly impressive to see this development being done in so short amount of time. I honestly wasn't expecting this so soon. Here is hoping that the AI will participate in GSL in 2 years, AlphaStar fighting!! I am so bored of this response. Anyone who doesn't immediately embrace machine learning or computer assistance is immediately scared of the future, or as you put it "human feelings hurt". In reality it is those of us who can think critically, look at the parameters of this program/show match and realise it isn't as impressive as the title makes it out to be. No one here will underplay or doubt the importance of "AI" in the modern world but this is a PR stunt that tries to overplay the result. | ||
Poopi
France12906 Posts
On January 29 2019 21:50 KelsierSC wrote: I am so bored of this response. Anyone who doesn't immediately embrace machine learning or computer assistance is immediately scared of the future, or as you put it "human feelings hurt". In reality it is those of us who can think critically, look at the parameters of this program/show match and realise it isn't as impressive as the title makes it out to be. No one here will underplay or doubt the importance of "AI" in the modern world but this is a PR stunt that tries to overplay the result. Plus quite a lot of researchers in machine learning / AI are a bit pissed off by Deepmind dishonesty / PR stunt, which could be hurting the field, so acting like every people expressing criticism is doing so because they don't understand AI is a bit laughable. | ||
|
deacon.frost
Czech Republic12129 Posts
On January 29 2019 22:10 Poopi wrote: Plus quite a lot of researchers in machine learning / AI are a bit pissed off by Deepmind dishonesty / PR stunt, which could be hurting the field, so acting like every people expressing criticism is doing so because they don't understand AI is a bit laughable. Well, the issue is that some responses qualify for that response. Edit- screw that proverb | ||
|
Subflow
52 Posts
I think I heard some of the programmers saying, that now they are going back to their general research... I guess it would make sense for their Image. Better leave with a 10-1 than getting crushed over and over again, once players find out and abuse the obvious weaknesses Alphastar has (warpprism harass, not giving respect to sentries, just for example) | ||
|
gtbex
Poland39 Posts
It seems like this. | ||
|
shabby
Norway6402 Posts
On January 29 2019 23:45 Subflow wrote: Will there be more games of Alphastar playing vs Pros? I think I heard some of the programmers saying, that now they are going back to their general research... I guess it would make sense for their Image. Better leave with a 10-1 than getting crushed over and over again, once players find out and abuse the obvious weaknesses Alphastar has (warpprism harass, not giving respect to sentries, just for example) Doubt it would get crushed, it will only get better over time. I think they said it had been training for around 1 week before TLO match and then another few days before MaNa match? Thats not a very long time, and it was markedly better in MaNas matches. I dont doubt that if they keep working on it and let it practice, it will eventually be as unbeatable as computers are in other games. | ||
|
Sphairos
22 Posts
[B] I dont doubt that if they keep working on it and let it practice, it will eventually be as unbeatable as computers are in other games. I highly doubt that. | ||
|
Subflow
52 Posts
| ||
|
Zzoram
Canada7115 Posts
On January 30 2019 01:55 shabby wrote: Doubt it would get crushed, it will only get better over time. I think they said it had been training for around 1 week before TLO match and then another few days before MaNa match? Thats not a very long time, and it was markedly better in MaNas matches. I dont doubt that if they keep working on it and let it practice, it will eventually be as unbeatable as computers are in other games. That’s not really how it works. They can train it for months, but if they haven’t improved the underlying learning capabilities, it won’t matter. They might have stopped after one or two weeks because there was no additional improvement after that. | ||
|
BronzeKnee
United States5218 Posts
On January 29 2019 19:11 MadMod wrote: Even with the insane micro I think MaNa goes 100-0 after training against it a bit. He just needed to unlearn to engage in razors edge micro battles. There are some huge holes in AlphaStars game. Some can be teased out easily (like learning to defending Warp Prism drops), but others won't be so easy. I hope Blizzard lets anyone play against AlphaStar via the SC2 client. That'd be awesome. | ||
|
neutralrobot
Australia1025 Posts
Someone on r/machinelearning made an interesting post pointing out that since the agents started off learning from human replays, they seem to have taken on the human trait of spamming and never quite been able to drop it. I haven't checked that it's right, but the post gave examples of times where you could clearly see spamming from A*. If true, this would mean that at best, epm==apm only some of the time. Are there spam clicks even while microing intensively? Is that part of why such high apm spikes are needed? | ||
|
maybenexttime
Poland5700 Posts
What I'm wondering is whether they could make an evolutionary algorithm that is trained to reconstruct a replay from one player's perspective. It's very different from simply teaching it to win. Such an approach would teach it how to model the state of the game from incomplete information. The main problem would be quantifying how faithful the reconstruction of a replay is. Then they could turn it into a module and incorporate it into AlphaStar, and make it model the game it is currently playing in real time (assuming it can simulate numerous games of SC2 that quickly). It could come up with realistic scenarios explaining what the AI already knows about the opponent. It could create working hypotheses regarding what has been happening behind the fog of war, and perhaps even verify them via scouting. Is what I'm proposing very far-fetched? | ||
|
cpower
228 Posts
On January 28 2019 21:15 Polypoetes wrote: But an AI doesn't get fatigued. Why would you hard-code in artificial fatigue so that the NN develops to avoid the effect of fatigue that it doesn't suffer from in the first place? Also, I don't think even for a human playing a Bo5, fatigue plays a big role. Unless you are jet-lagged or something. I assume you mean mental fatigue, which is hard to notice yourself. From my experience, humans have no obvious problems concentrating for 5x30 minutes. I don't understand why you say that an AI is not useful unless it has all the flaws humans have. I may have put in in a wrong way but misclicks do happen a lot in real games and AI is not designed to have misclicks so it's not really a fair battle to start with. I actually have talked with some developers on this program and see if they will try to implement that in the next phases. | ||
|
Grumbels
Netherlands7031 Posts
On January 30 2019 09:51 maybenexttime wrote: Does anyone know what game speed AlphaStar is playing at during its internal games? Do I remember correctly that they mentioned 200 years of experience in a week? Was it combined playtime across all agents? What I'm wondering is whether they could make an evolutionary algorithm that is trained to reconstruct a replay from one player's perspective. It's very different from simply teaching it to win. Such an approach would teach it how to model the state of the game from incomplete information. The main problem would be quantifying how faithful the reconstruction of a replay is. Then they could turn it into a module and incorporate it into AlphaStar, and make it model the game it is currently playing in real time (assuming it can simulate numerous games of SC2 that quickly). It could come up with realistic scenarios explaining what the AI already knows about the opponent. It could create working hypotheses regarding what has been happening behind the fog of war, and perhaps even verify them via scouting. Is what I'm proposing very far-fetched? I don't know if I'm understanding you correctly, but you could imagine some sort of implementation where an AI has a belief about the opponent's units and economy, which it acts upon in a game and then verifies via watching the replay. I haven't read the paper they released yet, but from some comments I read I don't think it has these capabilities currently. Also, I don't like spreading misinformation, but I /recall/ having heard that the figure of 200 years is the playtime of the agent which has played the longest time. The week of training probably also includes the initial stage of imitation learning from replays. Depending on how long this lasted, it would mean that if the agent playing vs TLO had 200 years of practice, then the one playing vs Mana, which trained for another week, would have at least 400 years of experience, but possibly much more. But it might be best to read the paper. I mean, the ratio of a week to 200 years is like 1 : 10,000 , and I'm pretty sure you can't speed up SC2 that much even with good hardware and eliminating graphics. So a single agent has to be able to train in parallel with itself. | ||
|
shabby
Norway6402 Posts
On January 30 2019 08:38 Zzoram wrote: That’s not really how it works. They can train it for months, but if they haven’t improved the underlying learning capabilities, it won’t matter. They might have stopped after one or two weeks because there was no additional improvement after that. I did say "keep working on it" though in addition to letting it practice. Of course its not "done" yet, or this wouldnt just be a PR demo. | ||
| ||
