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Learning Machine Learning for Game Development

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Re: Learning Machine Learning for Game Development

#21

Post by malka »

Jasper_ wrote: post #15
adkadskhj wrote: post #11

Earlier quoted context omitted.

Personally, i'd like to see AI used for "simple" things, not often tied to the core gameplay loop itself. Ie conceptually if you could tie the right gameplay components to an RNG function then the user could get new/unexpected experiences for a longer time. The problem is RNG often makes crap outputs. I'd be curious to see ML take this problem. I imagine the examples are limitless, but one that comes to mind might be…

ML is a system that takes a large data set, and an error function, and finds a generated output that minimizes the loss. What data set and error function are you proposing for "RNG"?

You feed it rng created world that were curated by humans.

Re: Learning Machine Learning for Game Development

#22

Post by Quarrelsome »

CJefferson wrote: post #3

I'm in this research area, it's facinating, but also (like many things in AI) easy to get wrong. The trickiest bit is deciding exactly what you want your AI to optimise for -- If you want a game where "anyone can win", you end up with coin toss. If you want a game where "skill is everything", you can accidentally end up with "who can click fastest". On a project I recently worked on (and I'm writing up), we ended up…

I feel like some of these questions are a bit backwards. Isn't it better to give the model an arena to play within and try to make all of the options _viable_? I.E. Balance is maintained when the average of the weapons the gladiators choose to take into the arena are diverse and have less bearing on the win outcome?

This means that rather focusing on what is merely broken one is focusing on both ends, what is broken and what is useless, attempting to push both towards an average of usability.

Personally it astonishes me that groups like WOTC (who produce MTG) don't appear to be using such models given the quantity of mistakes they seem to continue to make in game design.

Re: Learning Machine Learning for Game Development

#25

Post by sillysaurusx »

One really cool aspect of this:

For the actual game state representation that the model would receive as input, we found that passing an "image" encoding to the CNN resulted in the best performance, beating all benchmark procedural agents and other types of networks (e.g. fully connected).

It sounds like they literally decompose the game screen into inputs. E.g. in FPS games, your health is often displayed in the exact same spot; it passes a screenshot of that into the network.

Or maybe not. Maybe they decode the game state into an "image" in the sense that health is represented as a single pixel that ranges from RGB 0,0,0 to RGB 255,255,255. That would make more sense, but it's also slightly less exciting. Theoretically the model should be able to infer what health means simply by having enough experience, even if it's a more complex representation like Arabic numerals rather than a handcrafted input.

Anyone know if there are any other details, like model weights (ha ha, not likely), an architecture diagram, a paper, or some code snippet from some prototype that the researchers used for inspiration? The "image" representation is really quite interesting to me, since I hadn't thought about feeding data to networks that way. Theoretically a GAN could learn a thing or two about the world from having this sort of "image" input too.

Re: Learning Machine Learning for Game Development

#26

Post by kevinstubbs »

Cool to see this. I and surely many others in game development independently thought of doing this 5+ years ago. Fascinating to think that no matter how much software the world writes, we will keep finding entirely new ways to add value by writing even that much more code.

Re: Learning Machine Learning for Game Development

#27

Post by meheleventyone »

Quarrelsome wrote: post #22
CJefferson wrote: post #3

I'm in this research area, it's facinating, but also (like many things in AI) easy to get wrong. The trickiest bit is deciding exactly what you want your AI to optimise for -- If you want a game where "anyone can win", you end up with coin toss. If you want a game where "skill is everything", you can accidentally end up with "who can click fastest". On a project I recently worked on (and I'm writing up), we ended up…

I feel like some of these questions are a bit backwards. Isn't it better to give the model an arena to play within and try to make all of the options _viable_? I.E. Balance is maintained when the average of the weapons the gladiators choose to take into the arena are diverse and have less bearing on the win outcome? This means that rather focusing on what is merely broken one is focusing on both ends, what is broken…

For something like a deck building game, if all decks are viable (competitive) then there may as well be no deck building component. In fact a game like that is probably a pretty muddy brown color in terms of card variety. That would adversely affect the business model of selling new cards as well.

Desirable qualities in a game in terms of how it is balanced are way more complex than just every option being viable and leading to an even win rate against a similarly skilled opponent.

Re: Learning Machine Learning for Game Development

#28

Post by Yenrabbit »

Letting these ML agents loose on your game is also a good way to discover bugs in the implementation that a human player might never run into. When I first messed about with RL[1] I quickly discovered this, with my 'AI' learning to abuse invalid moves to get a re-roll of the dice or to stay put when it should have been forced to move. [1] - https://datasciencecastnet.home.blog/2020/01/24/swoggle-part...

Re: Learning Machine Learning for Game Development

#30

Post by Synaesthesia »

sfjailbird wrote: post #18

The most obvious use of ML in this context would be to predict player actions in multiplayer games. Most such games extrapolate the actions of remote players in order to give the illusion that there is no lag between updates. I bet for many situations, a good ML implementation could make this work almost flawlessly. No idea if anyone is doing this yet. Also, the headline is wrong: It's 'Leveraging Machine Learning fo…

So how will it be able to predict if I will turn left or right next?
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