EPTAlphaStar released: Deepmind Research on Ladder - Page 8
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necrosexy
451 Posts
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ROOTFayth
Canada3351 Posts
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Equalizer
Canada115 Posts
Rather than developing more complex play it probably is prone to getting stuck fine tuning a local optima that it was initialized close to. I think people that are disappointed would be because they were hoping for it to find more unusual but effective strategies. In that respect AlphaGo probably looks more impressive but SC2 should be much harder to achieve that on. | ||
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Ronski
Finland266 Posts
On July 26 2019 09:42 Fecalfeast wrote: I count one person who did anything close to 'shitting' on alphastar so what do you mean by everyone? What are you disappointed by? I'm glad that an AI wasn't able to immediately solve my favourite game even if it is playing at quite a high level in many aspects already. On July 26 2019 12:28 ROOTFayth wrote: you may want to try and understand how AI works Ronski, it's not magic, it takes time to solve Definitely not an expert about AI learning but watching the protoss agent play it was scouting at least. So expecting the same from Zerg and Terran AI wouldn't be too much. I just want to see the AI "playing the game", in all aspects of the game that is starcraft, making decisions and reacting to what the opponent is doing. Instead it just does the thing it knows and has no plan B if things go wrong. | ||
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PresenceSc2
Australia4032 Posts
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deacon.frost
Czech Republic12128 Posts
On July 26 2019 14:16 Ronski wrote: Definitely not an expert about AI learning but watching the protoss agent play it was scouting at least. So expecting the same from Zerg and Terran AI wouldn't be too much. I just want to see the AI "playing the game", in all aspects of the game that is starcraft, making decisions and reacting to what the opponent is doing. Instead it just does the thing it knows and has no plan B if things go wrong. Why would you scout as a Terran? And in the game I saw Deepmind was running through the whole map with a reaper, so it's not like it wasn't scouting at all. But generally, why? It appears Deepmind went for mass tanks marines with a wall(some times questionable  ). This holds most of the Protoss builds except the BS builds like proxy tempest. It would be interesting to see what it would do against a proxy tempest, but generally speaking this holds any ground attack as long as you keep a scan for DT(or build some turrets)If your plan covers most of the early game shenanigans anyway, the scout isn't needed per se. it helps to lower the cost of the defense but generally it's not that much needed. You must remember that it played maybe millions of games where the most successfull AIs were battling each other. In the end it moves to some builds/units which solve most of the issues so the AI then looks only for something that needs a different reaction. If everythign it knows in first 5 minutes can be solved with "more roaches, drones, ovies" then it will be using this build until somebody shows it - well, not everything is solved with roaches, you know?  The fact it has high winrate without scouting just proves the thought. + Show Spoiler + I am nowhere near high level player but I don't scout in TvP. Why would I? I build marines and tanks which cover everything except proxy tempest. And unless I stumble upon the proxy location I am not gonna scout what exactly is the Protoss proxying anyway  (and in TvZ I scout just to know if it's safe to build CC on the lowground) Any good player will beat me because I play very greedy and very, uhm, specific style, but I survive every early game shenanigans with the worker lead(what happens then is usually I royally screw up ) | ||
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Muliphein
49 Posts
This AI is a neural network so that means it doesn't reason. It doesn't string together thoughts. It doesn't do deductive reasoning. Yet it has a very high winrate. Something people only a while ago thought was impossible. But the AI does actually give insights in the game. First of all, it is an AI that crunches numbers in a cold unpassionate fashion. It doesn't care if it wins ugly. As a human, you want to play out every battle properly and win as decisively as possible. The AI does not. When it sends in it's units and sees the battle is won, it will no longer prioritize microing that battle when there are other things that improve winrate more. We saw this a lot in Go, where the AI started to play 'sub-optimal' when it was in a winning position. A neural network playing strangely when it is already won is perfectly understandable given the nature of the AI. It means that either the nodes aren't weighted properly for winning a won game harder and faster. Using your weights for these scenarios would limit the number of options you have to get proper weights for nodes that are more important to the outcome of the game. But this AI definitely indicates some things where humans may be completely wrong. Like scouting. People keep saying that scouting is really important. But the AI seems to disagree. This means that in the millions of AI vs Ai games, scouting doesn't increase AI winrate. I think this is not because the deep neural network is incapable of getting an architecture where seeing an opponent's tech tree will completely change the units being build. We do see the Protoss AI do this. Apparently the best way to play the game, for the AI, is to use a build that plays well vs most enemy builds and perfect that. Personally, I think humans are too obsessed by what the meta is, by mindgames, by scouting, by guessing what their opponent is doing. You really see fashion trends in which builds are popular. And people seem to suggest you are better off playing fashionable builds. But game theory wise you should actually play out of fashion builds. So this whole system humans made up around builds and coming up with new ones and beating the meta is completely artificial. It is humans adding a layer on top of the game because by their neurology they are forced to do so. An AI will be free of this limitation. Now I think that an AI will have a huge advantage in not getting tired, playing consistently, not being emotional, having an iron concentration. I think the main achievement is that they can get neural networks to converge so they are able to play this game at an extremely high level. That their play is filled with mistakes and questionable behavior only shows how much more improvement would still be possible. For example, building placement for terran is a problem that neural networks just cannot handle. It doesn't generalize. I know it is easier in SC2, but in SC BW, no one really can reason which walls are good walls. You have to use trial and error. There is no obvious general rule the human brain was able to pick up on. Same with protoss PvZ building placement. You memorize it for each starting location in each map. The AI will have to do the same thing. Now that they have a core neural network that works really well, they can try to add layers, either neural networks or hardcoded or other machine learning methods, to guide the neural network. I know Deepmind likes to have a single neural net be able to do unguided learning. So I think they will try that instead. This is why they went from their hybrid AlphaGo to their more pure AlphaZero. I also think people underestimate the decision making required in deciding if a battle can be won, and then winning it the way it saw how it was winnable. Especially when you also consider that the solution was to be convergable. I think this is the main problem to solve in RTS games; when do you engage a battle and when do you avoid. Making as many units as possible and deciding which units to build are kind of trivial decisions to that. I think that as more progress is made, we will see AI that we would have considered as 'eerie the same way we have in Go or Chess. And one of the reasons these AIs don't look that strong is because they need to play with human limitations. The one that beat Mana and TLO did not. But I agree that right now, we are not there yet. But people seem to be missing the point that perfect SC2 looks very different from what people tried to achieve. | ||
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Ronski
Finland266 Posts
On July 27 2019 18:18 Muliphein wrote: I have only seen a few games, but I understand why people are a bit disappointed. The AI isn't doing stuff that is insightful to us. At least not on face value. But the main point here is the winrate. It doesn't matter if the AI does stupid stuff, like terran building placement. This AI is a neural network so that means it doesn't reason. It doesn't string together thoughts. It doesn't do deductive reasoning. Yet it has a very high winrate. Something people only a while ago thought was impossible. But the AI does actually give insights in the game. First of all, it is an AI that crunches numbers in a cold unpassionate fashion. It doesn't care if it wins ugly. As a human, you want to play out every battle properly and win as decisively as possible. The AI does not. When it sends in it's units and sees the battle is won, it will no longer prioritize microing that battle when there are other things that improve winrate more. We saw this a lot in Go, where the AI started to play 'sub-optimal' when it was in a winning position. A neural network playing strangely when it is already won is perfectly understandable given the nature of the AI. It means that either the nodes aren't weighted properly for winning a won game harder and faster. Using your weights for these scenarios would limit the number of options you have to get proper weights for nodes that are more important to the outcome of the game. But this AI definitely indicates some things where humans may be completely wrong. Like scouting. People keep saying that scouting is really important. But the AI seems to disagree. This means that in the millions of AI vs Ai games, scouting doesn't increase AI winrate. I think this is not because the deep neural network is incapable of getting an architecture where seeing an opponent's tech tree will completely change the units being build. We do see the Protoss AI do this. Apparently the best way to play the game, for the AI, is to use a build that plays well vs most enemy builds and perfect that. Personally, I think humans are too obsessed by what the meta is, by mindgames, by scouting, by guessing what their opponent is doing. You really see fashion trends in which builds are popular. And people seem to suggest you are better off playing fashionable builds. But game theory wise you should actually play out of fashion builds. So this whole system humans made up around builds and coming up with new ones and beating the meta is completely artificial. It is humans adding a layer on top of the game because by their neurology they are forced to do so. An AI will be free of this limitation. Now I think that an AI will have a huge advantage in not getting tired, playing consistently, not being emotional, having an iron concentration. I think the main achievement is that they can get neural networks to converge so they are able to play this game at an extremely high level. That their play is filled with mistakes and questionable behavior only shows how much more improvement would still be possible. For example, building placement for terran is a problem that neural networks just cannot handle. It doesn't generalize. I know it is easier in SC2, but in SC BW, no one really can reason which walls are good walls. You have to use trial and error. There is no obvious general rule the human brain was able to pick up on. Same with protoss PvZ building placement. You memorize it for each starting location in each map. The AI will have to do the same thing. Now that they have a core neural network that works really well, they can try to add layers, either neural networks or hardcoded or other machine learning methods, to guide the neural network. I know Deepmind likes to have a single neural net be able to do unguided learning. So I think they will try that instead. This is why they went from their hybrid AlphaGo to their more pure AlphaZero. I also think people underestimate the decision making required in deciding if a battle can be won, and then winning it the way it saw how it was winnable. Especially when you also consider that the solution was to be convergable. I think this is the main problem to solve in RTS games; when do you engage a battle and when do you avoid. Making as many units as possible and deciding which units to build are kind of trivial decisions to that. I think that as more progress is made, we will see AI that we would have considered as 'eerie the same way we have in Go or Chess. And one of the reasons these AIs don't look that strong is because they need to play with human limitations. The one that beat Mana and TLO did not. But I agree that right now, we are not there yet. But people seem to be missing the point that perfect SC2 looks very different from what people tried to achieve. I think its completely possible that the AI will also just hit an iron wall at 6k MMR where it can no longer win because of its limited scouting. Hoping that is the case | ||
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Kenny_mk1
31 Posts
On July 27 2019 18:18 Muliphein wrote: I have only seen a few games, but I understand why people are a bit disappointed. The AI isn't doing stuff that is insightful to us. At least not on face value. But the main point here is the winrate. It doesn't matter if the AI does stupid stuff, like terran building placement. This AI is a neural network so that means it doesn't reason. It doesn't string together thoughts. It doesn't do deductive reasoning. Yet it has a very high winrate. Something people only a while ago thought was impossible. But the AI does actually give insights in the game. First of all, it is an AI that crunches numbers in a cold unpassionate fashion. It doesn't care if it wins ugly. As a human, you want to play out every battle properly and win as decisively as possible. The AI does not. When it sends in it's units and sees the battle is won, it will no longer prioritize microing that battle when there are other things that improve winrate more. We saw this a lot in Go, where the AI started to play 'sub-optimal' when it was in a winning position. A neural network playing strangely when it is already won is perfectly understandable given the nature of the AI. It means that either the nodes aren't weighted properly for winning a won game harder and faster. Using your weights for these scenarios would limit the number of options you have to get proper weights for nodes that are more important to the outcome of the game. But this AI definitely indicates some things where humans may be completely wrong. Like scouting. People keep saying that scouting is really important. But the AI seems to disagree. This means that in the millions of AI vs Ai games, scouting doesn't increase AI winrate. I think this is not because the deep neural network is incapable of getting an architecture where seeing an opponent's tech tree will completely change the units being build. We do see the Protoss AI do this. Apparently the best way to play the game, for the AI, is to use a build that plays well vs most enemy builds and perfect that. Personally, I think humans are too obsessed by what the meta is, by mindgames, by scouting, by guessing what their opponent is doing. You really see fashion trends in which builds are popular. And people seem to suggest you are better off playing fashionable builds. But game theory wise you should actually play out of fashion builds. So this whole system humans made up around builds and coming up with new ones and beating the meta is completely artificial. It is humans adding a layer on top of the game because by their neurology they are forced to do so. An AI will be free of this limitation. Now I think that an AI will have a huge advantage in not getting tired, playing consistently, not being emotional, having an iron concentration. I think the main achievement is that they can get neural networks to converge so they are able to play this game at an extremely high level. That their play is filled with mistakes and questionable behavior only shows how much more improvement would still be possible. For example, building placement for terran is a problem that neural networks just cannot handle. It doesn't generalize. I know it is easier in SC2, but in SC BW, no one really can reason which walls are good walls. You have to use trial and error. There is no obvious general rule the human brain was able to pick up on. Same with protoss PvZ building placement. You memorize it for each starting location in each map. The AI will have to do the same thing. Now that they have a core neural network that works really well, they can try to add layers, either neural networks or hardcoded or other machine learning methods, to guide the neural network. I know Deepmind likes to have a single neural net be able to do unguided learning. So I think they will try that instead. This is why they went from their hybrid AlphaGo to their more pure AlphaZero. I also think people underestimate the decision making required in deciding if a battle can be won, and then winning it the way it saw how it was winnable. Especially when you also consider that the solution was to be convergable. I think this is the main problem to solve in RTS games; when do you engage a battle and when do you avoid. Making as many units as possible and deciding which units to build are kind of trivial decisions to that. I think that as more progress is made, we will see AI that we would have considered as 'eerie the same way we have in Go or Chess. And one of the reasons these AIs don't look that strong is because they need to play with human limitations. The one that beat Mana and TLO did not. But I agree that right now, we are not there yet. But people seem to be missing the point that perfect SC2 looks very different from what people tried to achieve. Humans doesnt do scout meta and build because of neurology, but because they determined those were the fastest to achieve timing attack with certains upgrades, and that having the earliest composition with those criteria allow them to do well, unless the opponnents bo exploit a weakness in the bo. But some bo shouldnt have, and are designed to react against cheese (16 marine drop i guess?) Also without scouting you might get good stats on a ladder, but on a offline tourney its another story. Nevertheless good points on the rest of the post | ||
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MrFreeman
207 Posts
Still, it is very impressive how solid the gameplay is for a bot. | ||
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Jan1997
Norway671 Posts
This only works in GM though, below that you don't really see the same players over and over again. | ||
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Kalera
United States338 Posts
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Muliphein
49 Posts
On July 27 2019 19:58 Kenny_mk1 wrote: Humans doesnt do scout meta and build because of neurology, Everything humans do is because of their neurology. And no, I am not equivicating now. The point is that the AI doesn't have it. It just crunches millions of huge arrays of data until it finds an array that apparently wins a lot, for whatever reason. ...but because they determined those were the fastest to achieve timing attack with certains upgrades, and that having the earliest composition with those criteria allow them to do well, unless the opponnents bo exploit a weakness in the bo. But some bo shouldnt have, and are designed to react against cheese (16 marine drop i guess?) Also without scouting you might get good stats on a ladder, but on a offline tourney its another story. Nevertheless good points on the rest of the post But you are also playing against a human. When humans play against humans they cannot ignore neurology/psychology/mind games/meta because their opponent isn't and it is questionable if a human could even play that way. No human would have the approach to play one build vs everyone always and fine tune it. Humans are obsessed with strategy. We saw this when people debated automation in SC2. We see it in how there is even a meta. Having a meta is irrational in itself. Having fashionable builds is a property of the community, not of the game. Deepmind's work so far may be showing us that all this energy human players put into builds and strategies and mindgames could be a waste of time and effort. This is perfectly shown in the comment made saying that "for an AI to play perfect, it needs to take into consideration the style of the player." Why? The AI itself has no style. Why would it be a good thing for an AI to learn and study the style of player? This is where humans go wrong all the time, tricking themselves and overthinking things, leading to the wrong decision. Why do you want the AI to copy that? Maybe if you had perfect information about all games all their human players ever played. But that is not available. And the computational cost associated with that compared to the gain is clearly not worth it. The strength of the AI is that exactly it doesn't try to engage in mindgames and use generally subpar play because it thinks it will be superior to this specific player. There are a lot of cases where making a mistake can help you win quicker, because your opponent is making worse mistakes. But it is folly to try to make an AI that makes marginal plays based on a calculation that they think their opponent won't be able to exploit it. Maybe this is necessary in poker. There is a poker AI now that beats top players. But RTS is not poker and there is no reason for AI researchers to program their AI to make weak plays that could lead to quicker victories vs weak players. You may not like the way these AIs play. But they do show every very well what is most crucial to winning games. As for whether this AI approach has hit an iron wall, I think these bots must be converged to their maximum MMR in their internal way of measuring it. Otherwise, they wouldn't put them out there. And they are not trying to see where the strong points and weak points of their AIs are so they know how to change the neural net architecture to get better convergence. But there is no fundamental limitation in AI about why an AI cannot learn building placement, cannot learn to build hydras or mutas vs warp rays, etc etc. Yes, a neural network has inherent limitations. It is impossible to solve many different problems with the same neural net perfectly. If you decide to go the neural net approach you accept that there will be gaps in the generalizations the network makes, and you accept these gaps because of how good the net is in the general cases. But there is no reason why you cannot have a hybrid approach. In theory, you could have 10 000 neural networks, all trained to play the game at a very high level, but all quite different. And for every game state, the AI can play out the game for the next 5 seconds (of the entire game if you have infinite computation) using every neural network vs every other neural network. And then pick the best neural net from those simulations and use those actions vs the actual human. It should also be possible to course grain the game and have a quick approximation about the outcome of the game. It is clear that the terran AI has a problem vs air units. It would be easy to very quickly play out the game until the end using the current game state. In fact, the neural network doesn't even do this. It doesn't keep track of how many units the enemy has explicitly. It also doesn't try to figure out if it has to defend or attack explicitly. This could in principle be programmed in. Deepmind is probably not interested in this, as they want to figure out how to develop AI that can learn unassisted without prior knowledge and without human intervention. But AlphaGo that beat Lee Sedol had three different neural networks, not one, doing different things. If you are disappointed that the AI plays 1 build and doesn't scout while winning a lot, you are not disappointed in the AI. It does the only thing it cares for; winning. You are actually disappointed in the nature of SC2 because the AI shows the optimal way to play is not to scout and not to strategize. | ||
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AttackZerg
United States7453 Posts
I will disagree with the poster above me, the last statement. Since no professional sc2 players are managing the project and the bots are clearly levels below are top human players.... I think calling any of alphastars choices optimal is both premature and wrong. It is unique but.... it is far from the stage where we can make that claim. Different topic - It seems to me that the apm limit has had a detrimental effect that is manifest in scouting. The zerg spreads creep poorly, often injects badly and had no concept of overlord placement in the early midgame, the strategies imho are the incestuous offspring of the decision to speed limit. I see 350-550 apm range for most top humans. I think at a certain level if you arent 300+.... you are unable to properly manage zerg. Alphago and alphazero were not required to lower their skillsets to match the playerbase. I think they had catered too much to us and have thus moved away from their most historic achievements. First make a bot that can beat serral, maru and classic. Then consider the ethics and fairness of the approach. First they smashed Stockfish with very unfair parameters then months later (maybe a full year) they released a full match with a more level playing field.... even better victories for alphazero (almost non for black) and even some loses. I am not an expert in AI or anything else, just watching them treat this playerbase very differently and not shockingly, this different treatment has yielded a less historic or dominant AI. No human will ever beat alphago or alphazero. No reason to limit the game approach by human limitation. Like I said, add that nonsense later. God mode full speed zerg please. 8) (I am thankful for this great project and the people behind it. Cheers) | ||
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NinjaNight
428 Posts
On July 27 2019 18:18 Muliphein wrote: But this AI definitely indicates some things where humans may be completely wrong. Like scouting. People keep saying that scouting is really important. But the AI seems to disagree. This means that in the millions of AI vs Ai games, scouting doesn't increase AI winrate. I think this is not because the deep neural network is incapable of getting an architecture where seeing an opponent's tech tree will completely change the units being build. We do see the Protoss AI do this. ? You mentioned yourself it doesn't reason or deduce because it's a number crunching neural network. So naturally it's not going to be able to take advantage of scouting which requires high level reasoning to be useful. Of course scouting is not going to increase its winrate. It's also still far below pro level and it still has very little intelligence and mostly relies on efficient mechanics. It's not telling us anything yet about how Starcraft should be played. | ||
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Xain0n
Italy3963 Posts
On July 28 2019 06:57 AttackZerg wrote: I am really enjoying the thoughtful posts here. Thanks for the read. I will disagree with the poster above me, the last statement. Since no professional sc2 players are managing the project and the bots are clearly levels below are top human players.... I think calling any of alphastars choices optimal is both premature and wrong. It is unique but.... it is far from the stage where we can make that claim. Different topic - It seems to me that the apm limit has had a detrimental effect that is manifest in scouting. The zerg spreads creep poorly, often injects badly and had no concept of overlord placement in the early midgame, the strategies imho are the incestuous offspring of the decision to speed limit. I see 350-550 apm range for most top humans. I think at a certain level if you arent 300+.... you are unable to properly manage zerg. Alphago and alphazero were not required to lower their skillsets to match the playerbase. I think they had catered too much to us and have thus moved away from their most historic achievements. First make a bot that can beat serral, maru and classic. Then consider the ethics and fairness of the approach. First they smashed Stockfish with very unfair parameters then months later (maybe a full year) they released a full match with a more level playing field.... even better victories for alphazero (almost non for black) and even some loses. I am not an expert in AI or anything else, just watching them treat this playerbase very differently and not shockingly, this different treatment has yielded a less historic or dominant AI. No human will ever beat alphago or alphazero. No reason to limit the game approach by human limitation. Like I said, add that nonsense later. God mode full speed zerg please. 8) (I am thankful for this great project and the people behind it. Cheers) That's because Chess and Go don't have huge mechanical requirements to be played, unlike Sc2; we already had a glimpse of a machine capable of beating top players without limitations, AlphaStalker(which indeed DID have limitations). I don't think it would worth to spend time and money on a neural network that would beat humans macroing with perfect timing while microing with immaculate precision every single unit on the map, all at once. | ||
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Kenny_mk1
31 Posts
The meta in which cc first emerged was such, a meta because everyone played safe so it was low risk to do cc first. Launch a reinforcement ia in a ladder where all players open fe, it should open cc first because its what is best. Not studying what you expect from a player is just a part if chance to win you ignore, and is also what led to superb game on GSL. Also when the ia will get better it will probably be the best chance for human player to defeat alphastar : to abuse his weakness. If alphastar could ingest pro replay to learn it will know trap make good uses of oracles and wont need to scout to make defenses for example (why not? Doesnt it play thousand of game per hour? But very very different approach i guess) Flash fine tuned his 5rax +1 with mech transition and won over 2 years with that ( mb more) Then he fucked up his wrists and fine tuned 1 1 1to do a less intensive build. Every game was different but what was strong was the variation he could done. TvT lotv is pretty much the raven build those days. Sorry was on phone post isnt organized | ||
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AttackZerg
United States7453 Posts
On July 28 2019 08:59 Xain0n wrote: That's because Chess and Go don't have huge mechanical requirements to be played, unlike Sc2; we already had a glimpse of a machine capable of beating top players without limitations, AlphaStalker(which indeed DID have limitations). I don't think it would worth to spend time and money on a neural network that would beat humans macroing with perfect timing while microing with immaculate precision every single unit on the map, all at once. I think defining AI and limiting it based upon human capabilities creates a worse network and is counter to the approach they used in other games. In chess and go, the mechanics are internal. They didn't limit the depth of thinking to the 7 to 11 short term objects a human can hold simultaneously. In those genres, the goal was world dominating AI. They beat non top tier players on a specfic map. Very impressive but not near the accomplishments of beating Stockfish even in that first unfair match. They gave us a glimpse of True AI at the start. In chess the only game for alphazero is other machines. It is fine if that becomes true of this sport also. I love the project but am not convinced or in love with their approach to sc2. In chess and go, they gave no fucks about the respective communities and cultures - they wanted to rip down and conquer. It seems they are less focused on total domination in this genre. I accept, I may be wrong in both understanding and or communicating. Still just want to see a god zerg AI.... Been waiting since 99. Still waiting. | ||
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Inrau
35 Posts
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terribleplayer1
95 Posts
On July 28 2019 09:42 Inrau wrote: The agents winrates would plummet if players saw ALPHASTAR and players had a few weeks constantly playing. Yea, its being helped a ton by the barcode, and the insane mechanics, I can see it losing a ton of games to master leaguers if it does the same build time and time again, which is what it seems to be doing. I think each agent just hones in into one specific playstyle and sticks with it, if players know which agent they're playing they're going to have insane winrates vs it, despite it's micro/macro advantage, unless an agent can actually learn to play the game rather than just do a generic strong build, learn when to scout, what to scout, and how to react. Otherwise it's only chance is to randomize which agent is playing and that is still probably not going to be enough to face the likes of Serral/Maru. The APM limitations gotta stay, otherwise it's just not interesting at all, it simply breaks the game. | ||
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