The first step in the project was figuring out how to run Dota 2 in the cloud on a physical GPU. The game gave an obscure error message on GPU cloud instances. But when starting it on Greg’s personal GPU desktop (which is the desktop brought onstage during the show), we noticed that Dota booted when the monitor was plugged in, but gave the same error message when unplugged. So we configured our cloud GPU instances to pretend there was a physical monitor attached.
Dota didn’t support custom dedicated servers at the time, meaning that running scalably and without a GPU was possible only with very slow software rendering. We then created a shim to stub out most OpenGL calls, except the ones needed to boot.
This is an unquestionable technical achievement, but its important to keep in mind that the AI view of the world is through the bot API. This is compared to a human that decodes game state from observing the monitor. Not being an avid DOTA2 player I marvel at skilled players that can follow the game based on what looks like a very busy and colorful mess to me.
At the same time most of the deep learning field has been focusing on implementing super-human perceptual abilities (vision, hearing, translation) that come instinctually to humans. The higher level reasoning and memory/attention augmented machine learning is still cutting edge research. I think DeepMind and OpenAI are driving research towards that end.
By "unbeatable" they mean it lasted a few hours in the hands of the public before the hardcoded behaviors were exploited and it was defeated consistently. Turns out hardcoding the mechanism for players to manually block creeps (a more "creative" play style, but a fundamental element of lane control) allowed for the exploit. The bot did not teach itself to control the flow of creeps to gain a laning advantage against the opponent.
Not to diminish the teams' achievement. This was awesome. But again, it's just more over-hyping of AI that should be called out for what it really is.
They neither call it unbeatable nor mention hardcoding anything. The "unbeatable" is a quote from a professional tester, and they discuss the losses, acknowledging the need for further improvements for more general play.
Looks like they did hardcode the creep blocking behavior. Well, they hardcoded the reward for creep blocking and trained the bot specifically to learn behavior to maximize for that reward.
But that is not unfair. Humans receive plenty of curriculum training as well, we're not supposed to figure out the world by bumping into walls. Even in Dota2, the top players learned from observing each other how to deal with the bot. In fact, efficient retraining to include new strategies on the spot would be a very human-like learning ability.
Yeah but humans can more easily make macro-decisions based on micro situations. It's easier for us to look at a map and figure out which side is winning, sometimes that's really hard for a computer to do.
I'm not a Dota2 player, but like SC2 for example is a game with LOTS of room for AI improvements. I've always thought that having some sort of APM limit might actually encourage AI authors to adopt new and unique approaches to macro-strats, but it doesn't seem to be on the horizon.
When it comes to do a small thing rapidly, I think bots are almost always going to win.
When it comes to do something large-scale with finesse, I think humans are going to have an advantage for a LONG time.
I think that part of what makes human agents so effective at certain tasks, especially in the context of being up against another human is that we can evaluate an event and better understand the WHY of it relative to the player that played it.
If I see a player pull back a bit, sometimes I think to myself that maybe they saw something they weren't expecting or something they weren't quite sure how to handle. When a computer sees the same move, a floating point number among millions changes slightly. I can try and figure out why they might be pulling back, if I did something weird or if I did something totally normal I might suspect it is bait, etc. I can think all these things in a short period of time and while large AIs might have better FLOPS than me, it doesn't understand what I'm doing, why I do it, etc.
Curriculum learning isn't as effective in bots as it is in humans is my contention, I guess.
Fair/unfair is a pointless observation when it comes to humans vs bots. The diversity of human-based problem solving is the perfect friction to train AIs against, imo.
The question is whether AI would have ever learned creep blocking on its own. Or whether creep blocking is really as useful as people say. That is the key to the whole thing.
Yeah, the more I learn about this the less I'm impressed. Seems like they wanted to steal DeepMind's/Blizzard's thunder and hacked together a demo that isn't quite as advanced as they made it out to be.
To be fair, I think this is a normal reaction to hyped AI in gaming. I remember reading an article (comment chain?) some time ago on here about how a lot of people though Deep Blue would beat Kasparov through some sort of advanced understanding of the human mind, a new sort of intuition, etc. But ultimately it came down to being able to brute force the potential move space quickly. The games will change but I wonder if the overarching effectiveness of AI victory through computational brute force will.
> being able to brute force the potential move space quickly
MC sampling is an essential tool (when used together with simulation and neural nets). Don't dismiss it yet, it has a lot to offer to deep learning. For example, if you don't have enough data, you can create it by brute forcing / simulation.
If you watch the vids and read the blog posts and re watch it and reread it. You end up with just more and more questions. The bot learned to raze from the Fog Of War based on playing a pro once or twice? It changed its build based on losing once? (sure you say you white listed it but it still had choices no?). Wand not being a common opening is also not true. It's common AGAINST heavy casters. (I wont rush wand AS a bat rider but I would AGAINST him. I would AGAINST shadowfiend as well.) Was the animation canceling also taught similar to the creep block? independent RL?).
So many things don't fit in.. but that might be because I am neither good at DOTA 2 nor at AI stuff. Good luck with the 5v5's openai. And if the AI bots become public, maybe i'll get to play against them some day.
> It changed its build based on losing once? (sure you say you white listed it but it still had choices no?).
What I think happened here: the bot lost against someone using an item that hadn't been whitelisted before. The devs decided to include the item among the possible choices for self-training. Exploring the new choice, the bot adapted to using it to defeat its previous strategy. As a result, it now had to change its strategy to account for an opponent using the item.
As a developer I surely am amazed of the capabilities of the bot, but as a Dota player I am a bit more skeptic.
The bot only wins in a certain scenario, Shadow Fiend vs Shadow Fiend, no bottle, no runes, no jungle creeps and so on.
This cuts down the possibilities of playstyle a lot. This is stuff the pros are used to have available, and are training with. The bot was trained without it and has a fitting playstyle. For this certain type of 1v1, a pro would have to adapt. I still think that if you give them enough time, they will figure it out.
That is correct, what I was trying to say is, that the playstyle of the professional players is not used to those rules. It takes them out of their comfort zone and they have to adapt. I would love to see the bot matched up with a high mmr player who is playing matches with these rules on a regular basis
While you are obviously right that it's not a "true" match, I have to disagree that it takes them out of their comfort zone. Especially Arteezy (playing one of best Shadow Fiends), Sumail are extremely good mid players and both have actually participated in the solo 1v1 tournament I've linked. There's even an official 1v1 game mode in-game - I would argue that they both play these matches "on a regular basis".
I love the bot, but not, those matched are not played in regular basis, even a great part of the community is not happy with tournaments when they include those modes, the best example is the TI, the first version have a small tournament to see which was the best mid player, it was not very popular, and was removed.
I still agree and think that you are somewhat correct. Nevertheless: the pros are training 5v5 exclusively. 1v1 is a just for fun gamemode, the money is earned in the 5v5 tournaments.
I am not saying the bot should be able to play 5v5, I am saying that he ist trained to play this one very specific 1v1 scenario. He is doing really well with that, but as soon as you pick a different hero, the bot will struggle.
They didn't come up with the format by themselves. This is the "standard" 1v1 format. But yes as they acknowledged getting the bot to play 5v5 is a much bigger challenge.
Building a cooperative bot for 5v5 will impress me a lot more. People are rather good at this sort of thing and so far AI is more likely be become good at 1v1 games (Go, Chess, etc). If there is anything I've learned after 3 decades in this industry is that people are devious.
This is a highly editorialized title that clearly doesn't follow the posting guidelines:
> Otherwise please use the original title, unless it is misleading or linkbait.
> Please don't do things to make titles stand out ...
(the original title is the much more sedate "More on DOTA 2"; in case it is changed by moderators, at the time of this comment it says "How We Built the Unbeatable DOTA 2 bot", which is not even a quotation from the article)
The submitted title has certainly shaped the commentary (see people reacting to "unbeatable", and whether it's an impressive achievement, vs reacting to the technical content).
So, in a nutshell, I think we should avoid titles like this, and I believe the guidelines transparently say so as well.
Standard---maybe not objection, but complication: I'm waiting for the day AI can do this in 2100 KWh. (Human brain uses ~15W continuous. 15 W * 24h * 365 days * 16 years.)
Those who are thinking that AI is being overly hyped, and that the demos like these are not smart enough, think about it, today's AI is smarter enough IMO, as the potential of real-world applications of AI as of now itself could be vast. It just need some practical polished implementations. Did the Dota 2 bot learn the game from scratch by learning the rules all by itself? No, as evident from the post itself - "small amounts of “coaching” with self-play. But think about it. It need not be that smart to replace humans. IMO, many of our jobs do not need to very complicated self-learning. In real world day-day jobs like agriculture, factories, and to a great extend what we call as skilled labour like driving, management etc, the kind of work is often repetitive. AI is already better than humans at recognising speech, vision and to some extend languages. Combining these skills with mechanical robots in a meaningful way can easily replace what we, as humans can do, except in niche fields. Even there computer can offload our job in several ways. Robots/AI can do it much much faster, and much more efficient than us and it will only be getting better and better with overall advancement in research and the amount of data available..
So it's kind of interesting and equally scary that we are looking at a potential future, possibly near, where most of the work can be handled by a trained robot with humans only supervising them in exceptional cases. Robots can then could be only need to be smart enough to alert the human supervisors when they reach a state where they cannot perform the job normally, for e.g. power grid going off in a factory and they couldn't figure out what to do, it could only be a 0.001 % chance. The point is AI need not get as good as humans in learning complex scenarios to replace majority of us, it just need to do 99% of what we repeatedly do, much better than us.
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[ 2.1 ms ] story [ 77.9 ms ] threadMy favorite part:
The first step in the project was figuring out how to run Dota 2 in the cloud on a physical GPU. The game gave an obscure error message on GPU cloud instances. But when starting it on Greg’s personal GPU desktop (which is the desktop brought onstage during the show), we noticed that Dota booted when the monitor was plugged in, but gave the same error message when unplugged. So we configured our cloud GPU instances to pretend there was a physical monitor attached.
Dota didn’t support custom dedicated servers at the time, meaning that running scalably and without a GPU was possible only with very slow software rendering. We then created a shim to stub out most OpenGL calls, except the ones needed to boot.
At the same time most of the deep learning field has been focusing on implementing super-human perceptual abilities (vision, hearing, translation) that come instinctually to humans. The higher level reasoning and memory/attention augmented machine learning is still cutting edge research. I think DeepMind and OpenAI are driving research towards that end.
Not to diminish the teams' achievement. This was awesome. But again, it's just more over-hyping of AI that should be called out for what it really is.
But that is not unfair. Humans receive plenty of curriculum training as well, we're not supposed to figure out the world by bumping into walls. Even in Dota2, the top players learned from observing each other how to deal with the bot. In fact, efficient retraining to include new strategies on the spot would be a very human-like learning ability.
I'm not a Dota2 player, but like SC2 for example is a game with LOTS of room for AI improvements. I've always thought that having some sort of APM limit might actually encourage AI authors to adopt new and unique approaches to macro-strats, but it doesn't seem to be on the horizon.
When it comes to do a small thing rapidly, I think bots are almost always going to win.
When it comes to do something large-scale with finesse, I think humans are going to have an advantage for a LONG time.
I think that part of what makes human agents so effective at certain tasks, especially in the context of being up against another human is that we can evaluate an event and better understand the WHY of it relative to the player that played it.
If I see a player pull back a bit, sometimes I think to myself that maybe they saw something they weren't expecting or something they weren't quite sure how to handle. When a computer sees the same move, a floating point number among millions changes slightly. I can try and figure out why they might be pulling back, if I did something weird or if I did something totally normal I might suspect it is bait, etc. I can think all these things in a short period of time and while large AIs might have better FLOPS than me, it doesn't understand what I'm doing, why I do it, etc.
Curriculum learning isn't as effective in bots as it is in humans is my contention, I guess.
Fair/unfair is a pointless observation when it comes to humans vs bots. The diversity of human-based problem solving is the perfect friction to train AIs against, imo.
What about this tweet from OpenAI?
"Our Dota 2 AI is undefeated against the world's best solo players" [1]
Also Musk called it more complex than Go,
"OpenAI first ever to defeat world's best players in competitive eSports. Vastly more complex than traditional board games like chess & Go." [2]
[1] https://twitter.com/OpenAI/status/896157788908290048
[2] https://twitter.com/elonmusk/status/896163163581825025?lang=...
I think there is a fair argument for Dota being vastly more complex than Go but there almost certainly isn't for 1v1 SF mid.
MC sampling is an essential tool (when used together with simulation and neural nets). Don't dismiss it yet, it has a lot to offer to deep learning. For example, if you don't have enough data, you can create it by brute forcing / simulation.
So many things don't fit in.. but that might be because I am neither good at DOTA 2 nor at AI stuff. Good luck with the 5v5's openai. And if the AI bots become public, maybe i'll get to play against them some day.
If you tell the API to make you attack a thing once, you're 'attacking' until the projectile is created, and then you're 'idle' immediately afterward.
So long as you're feeding the hero actions, it'll always animation cancel.
What I think happened here: the bot lost against someone using an item that hadn't been whitelisted before. The devs decided to include the item among the possible choices for self-training. Exploring the new choice, the bot adapted to using it to defeat its previous strategy. As a result, it now had to change its strategy to account for an opponent using the item.
I am not saying the bot should be able to play 5v5, I am saying that he ist trained to play this one very specific 1v1 scenario. He is doing really well with that, but as soon as you pick a different hero, the bot will struggle.
How does the bot understand the value of long-term strategy?
> Otherwise please use the original title, unless it is misleading or linkbait.
> Please don't do things to make titles stand out ...
(the original title is the much more sedate "More on DOTA 2"; in case it is changed by moderators, at the time of this comment it says "How We Built the Unbeatable DOTA 2 bot", which is not even a quotation from the article)
The submitted title has certainly shaped the commentary (see people reacting to "unbeatable", and whether it's an impressive achievement, vs reacting to the technical content).
So, in a nutshell, I think we should avoid titles like this, and I believe the guidelines transparently say so as well.
So whilst the new edited title is a bit click-baity, it is nevertheless more meaninful than the original.
It's also a lie, given that the bot was beaten multiple times by plenty of different people.
So it's kind of interesting and equally scary that we are looking at a potential future, possibly near, where most of the work can be handled by a trained robot with humans only supervising them in exceptional cases. Robots can then could be only need to be smart enough to alert the human supervisors when they reach a state where they cannot perform the job normally, for e.g. power grid going off in a factory and they couldn't figure out what to do, it could only be a 0.001 % chance. The point is AI need not get as good as humans in learning complex scenarios to replace majority of us, it just need to do 99% of what we repeatedly do, much better than us.