Stream, FPV, and more data - Python plays GTA p.15

By: sentdex

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Uploaded on 05/08/2017

Our AI friend here, Charles, is a convolutional neural network that learns to drive through deep learning.

At the moment, Charles learns and takes all actions based on single frames at a time, and bases his decisions on just pixel data. Charles only sees exactly what you see.

In time, I intend to give Charles some short-term memory to hopefully improve his driving.

Text tutorials and sample code:
Project Github:

Comments (2):

By anonymous    2017-09-20

UPDATE 2017-01: The field is moving very fast since AlphaGo's success, and there are new frameworks to facilitate the development of machine learning algorithms on games almost every months. Here is a list of the latest ones I've found:

  • OpenAI's Universe: a platform to play virtually any game using machine learning. The API is in Python, and it runs the games behind a VNC remote desktop environment, so it can capture the images of any game! You can probably use Universe to play Diablo II through a machine learning algorithm!
  • OpenAI's Gym: Similar to Universe but targeting reinforcement learning algorithms specifically (so it's kind of a generalization of the framework used by AlphaGo but to a lot more games).
  • TorchCraft: a bridge between Torch (machine learning platform) and StarCraft: Brood War.
  • pyGTA5: a project to self-drive cars in GTA5 using only screen captures (with lots of videos online).

Very exciting times!

IMPORTANT UPDATE (2016-06): As noted by OP, this problem of training artificial networks to play games using only visual inputs is now being tackled by several serious institutions, with quite promising results, such as DeepMind Deep-Qlearning-Network (DQN).

And now, if you want to get to take on the next level challenge, you can use one of the various AI vision game development platforms such as ViZDoom, a highly optimized platform (7000 fps) to train networks to play Doom using only visual inputs:

ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular. ViZDoom is based on ZDoom to provide the game mechanics.

And the results are quite amazing, see the videos on their webpage and the nice tutorial (in Python) here!

There is also a similar project for Quake 3 Arena, called Quagents, which also provides easy API access to underlying game data, but you can scrap it and just use screenshots and the API only to control your agent.

Why is such a platform useful if we only use screenshots? Even if you don't access underlying game data, such a platform provide:

  • high performance implementation of games (you can generate more data/plays/learning generations with less time so that your learning algorithms can converge faster!).
  • a simple and responsive API to control your agents (ie, if you try to use human inputs to control a game, some of your commands may be lost, so you'd also deal with unreliability of your outputs...).
  • easy setup of custom scenarios.
  • customizable rendering (can be useful to "simplify" the images you get to ease processing)
  • synchronized ("turn-by-turn") play (so you don't need your algorithm to work in realtime at first, that's a huge complexity reduction).
  • additional convenience features such as crossplatform compatibility, retrocompatibility (you don't risk your bot not working with the game anymore when there is a new game update), etc.

To summarize, the great thing about these platforms is that they alleviate much of the previous technical issues you had to deal with (how to manipulate game inputs, how to setup scenarios, etc.) so that you just have to deal with the learning algorithm itself.

So now, get to work and make us the best AI visual bot ever ;)

Old post describing the technical issues of developping an AI relying only on visual inputs:

Contrary to some of my colleagues above, I do not think this problem is intractable. But it surely is a hella hard one!

The first problem as pointed out above is that of the representation of the state of the game: you can't represent the full state with just a single image, you need to maintain some kind of memorization (health but also objects equipped and items available to use, quests and goals, etc.). To fetch such informations you have two ways: either by directly accessing the game data, which is the most reliable and easy; or either you can create an abstract representation of these informations by implementing some simple procedures (open inventory, take a screenshot, extract the data). Of course, extracting data from a screenshot will either have you to put in some supervised procedure (that you define completely) or unsupervised (via a machine learning algorithm, but then it'll scale up a lot the complexity...). For unsupervised machine learning, you will need to use a quite recent kind of algorithms called structural learning algorithms (which learn the structure of data rather than how to classify them or predict a value). One such algorithm is the Recursive Neural Network (not to confuse with Recurrent Neural Network) by Richard Socher:

Then, another problem is that even when you have fetched all the data you need, the game is only partially observable. Thus you need to inject an abstract model of the world and feed it with processed information from the game, for example the location of your avatar, but also the location of quest items, goals and enemies outside the screen. You may maybe look into Mixture Particle Filters by Vermaak 2003 for this.

Also, you need to have an autonomous agent, with goals dynamically generated. A well-known architecture you can try is BDI agent, but you will probably have to tweak it for this architecture to work in your practical case. As an alternative, there is also the Recursive Petri Net, which you can probably combine with all kinds of variations of the petri nets to achieve what you want since it is a very well studied and flexible framework, with great formalization and proofs procedures.

And at last, even if you do all the above, you will need to find a way to emulate the game in accelerated speed (using a video may be nice, but the problem is that your algorithm will only spectate without control, and being able to try for itself is very important for learning). Indeed, it is well-known that current state-of-the-art algorithm takes a lot more time to learn the same thing a human can learn (even more so with reinforcement learning), thus if can't speed up the process (ie, if you can't speed up the game time), your algorithm won't even converge in a single lifetime...

To conclude, what you want to achieve here is at the limit (and maybe a bit beyond) of current state-of-the-art algorithms. I think it may be possible, but even if it is, you are going to spend a hella lot of time, because this is not a theoretical problem but a practical problem you are approaching here, and thus you need to implement and combine a lot of different AI approaches in order to solve it.

Several decades of research with a whole team working on it would may not suffice, so if you are alone and working on it in part-time (as you probably have a job for a living) you may spend a whole lifetime without reaching anywhere near a working solution.

So my most important advice here would be that you lower down your expectations, and try to reduce the complexity of your problem by using all the information you can, and avoid as much as possible relying on screenshots (ie, try to hook directly into the game, look for DLL injection), and simplify some problems by implementing supervised procedures, do not let your algorithm learn everything (ie, drop image processing for now as much as possible and rely on internal game informations, later on if your algorithm works well, you can replace some parts of your AI program with image processing, thus gruadually attaining your full goal, for example if you can get something to work quite well, you can try to complexify your problem and replace supervised procedures and memory game data by unsupervised machine learning algorithms on screenshots).

Good luck, and if it works, make sure to publish an article, you can surely get renowned for solving such a hard practical problem!

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