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TensorKart: self-driving MarioKart with TensorFlow

kevinhughes.ca

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Re: TensorKart: self-driving MarioKart with TensorFlow

#61

Post by JonnieCache »

dclowd9901 wrote: post #51
bryondowd wrote: post #34

Earlier quoted context omitted.

Also, the AI opponents don't have to play by the same rules. They go by fun > fairness to keep things interesting. That's why you normally can't keep a huge lead on AI opponents, because they "rubberband" back up to you faster than they should be able to. Wouldn't surprise me if they don't even 'drive' in any sense while off-screen, just increment some abstract position relative to the track length. But I don't know…

"Wouldn't surprise me if they don't even 'drive' in any sense while off-screen, just increment some abstract position relative to the track length. But I don't know this for a fact." This seems unlikely, especially given how item pickup zones operate. Since an item box disappears for a short period of time after someone drives over it, it's imperative that the position of the CPU player who drove over it, and the one…

This is indeed what they do. Mario Kart 64 is known for having the most egregious rubberbanding ever.

Re: TensorKart: self-driving MarioKart with TensorFlow

#62

Post by hkmurakami »

sgrove wrote: post #58
hkmurakami wrote: post #47

Earlier quoted context omitted.

The most amazing run I've seen so far was a RTA (realtime time attack) of mega man 2. A human player is manually collision glitching and writing over memory with a sequence of inputs. And the RTA time in 2016 is now faster than the initial TAS records.

Do you have a link? Sounds like it'd be a very interesting watch. Edit: I found one that has an example (I think) around 7:38 http://www.nicovideo.jp.am/watch/sm13963118 - the collision detection pushes megaman into the wall and jumps between different sections. Very interesting indeed!

Sorry I think the one I was thinking of was Megaman 2. I did find a link for you. Starts at 2:36. http://www.nicovideo.jp/watch/sm23825129

Also you might be interested in the Final Fantasy 6 memory overwrite bug that was discovered in 2016 as well. It uses the Window Color menu settings as the data reference.

Btw regarding the Megaman 2 RTA, there's an even more ridiculous collision bug being used around 11:30 http://www.nicovideo.jp/watch/sm28321223

Re: TensorKart: self-driving MarioKart with TensorFlow

#64

Post by holografix »

This is very cool and I think if Kevin spends a bit of time learning reinforcement learning it could be amazing.

It seems like a lot of people doing reinforcement learning on video games get bogged down on training on raw pixels only... it would take a tremendous amount of data to make the driver recognise when and where to use certain power ups, however if you encoded this as a variable, wow it could be really cool.

I believe this is fundamentally how we humans learn with so few examples. Other humans "encode features for our brain to track" by telling us how it should be done and what information to prioritise.

Re: TensorKart: self-driving MarioKart with TensorFlow

#65

Post by haikuginger »

xigency wrote: post #53

I'm interested in knowing why the Python and C components communicate with HTTP, beyond reading about the bugfix. Wouldn't it be easier to use sockets or files or some other mechanism to integrate the two languages? Just something to think about as a developer. I would imagine that on a local machine, using HTTP as the protocol might add latency.

This was my initial reaction as well; it seems like a raw socket or even embedding a Python interpreter would be better ways to go.

Re: TensorKart: self-driving MarioKart with TensorFlow

#66

Post by glidek »

cr0sh wrote: post #25

This is pretty cool; as someone who is currently working on the second project (traffic sign recognition) for the Udacity "Self-Driving Car Engineer" nanodegree, using TensorFlow - it is interesting to me how it seems like the "standard" MNIST CNN can be adapted to so many other use cases. For the project I am currently working on, I'm using a slightly modified form of LeNet - which isn't too different from the TF MN…

As someone who's interested in taking the Udacity course, would your recommend it? Do you think the course prepares you enough find a Self-Driving developer job? Would you learn enough to compete/work along side people who got their Masters/PhD in Machine Learning? Appreciate your input.

Re: TensorKart: self-driving MarioKart with TensorFlow

#67

Post by glidek »

jrheard wrote: post #33
melvinmt wrote: post #32

Earlier quoted context omitted.

> the Udacity "Self-Driving Car Engineer" nanodegree That looks like a great course by the way, thanks for sharing.

I'm one of cr0sh's classmates. I don't have any background in ML/AI/etc, so I've had to supplement the Udacity course materials with a lot of external resources (just finished watching the Stanford CS231n course, which was very helpful), but overall the course been really interesting+fun so far. It's really nice to be exposed to new kinds of tech I've never heard of / used before. Refreshing change from webdev. If yo…

As someone who's interested in taking the Udacity course, could you understand the course better by answering the following questions? Do you think the course prepares you enough find a Self-Driving developer job? Would you learn enough to compete/work along side people who got their Masters/PhD in Machine Learning? Appreciate your input.

Re: TensorKart: self-driving MarioKart with TensorFlow

#69

Post by cr0sh »

glidek wrote: post #66
cr0sh wrote: post #25

This is pretty cool; as someone who is currently working on the second project (traffic sign recognition) for the Udacity "Self-Driving Car Engineer" nanodegree, using TensorFlow - it is interesting to me how it seems like the "standard" MNIST CNN can be adapted to so many other use cases. For the project I am currently working on, I'm using a slightly modified form of LeNet - which isn't too different from the TF MN…

As someone who's interested in taking the Udacity course, would your recommend it? Do you think the course prepares you enough find a Self-Driving developer job? Would you learn enough to compete/work along side people who got their Masters/PhD in Machine Learning? Appreciate your input.

> As someone who's interested in taking the Udacity course, would your recommend it?

So far, yes - but that has a few caveats:

See - I have some background prior to this, and I think it biases me a bit. First, I was one of the cohort that took the Stanford-sponsored ML Class (Andrew Ng) and AI Class (Thrun/Norvig), in 2011. While I wasn't able to complete the AI Class (due to personal reasons), I did complete the ML Class.

Both of these courses are now offered by Udacity (AI Class) and Coursera (ML Class):

https://www.udacity.com/course/intro-to-artificial-intellige...

https://www.coursera.org/learn/machine-learning

If you have never done any of this before, I encourage you to look into these courses first. IIRC, they are both free and self-paced online. I honestly found the ML Class to be easier than the AI class when I took them - but that was before the founding of these two MOOC-focused companies, so the content may have changed or been made more understandable since then.

In fact, now that I think about it, I might try taking those courses again myself as a refresher!

After that (and kicking myself for dropping out of the AI Class - but I didn't have a real choice there at the time), in 2012 Udacity started, and because of (reasons...) they couldn't offer the AI Class as a course (while for some reason, Coursera could offer the ML Class - there must have been licensing issues or something) - so instead, they offered their CS373 course in 2012 (at the time, titled "How to Build Your Own Self-Driving Vehicle" or something like that - quite a lofty title):

https://www.udacity.com/course/artificial-intelligence-for-r...

I jumped at it - and completed it as well; I found it to be a great course, and while difficult, it was very enlightening on several fronts (for the first time, it clearly explained to me exactly how a Kalman filter and PID worked!).

So - I have that background, plus everything else I have read before then or since (AI/ML has been a side interest of mine since I was a child - I'm 43 now).

My suggestion if you are just starting would be to take the courses in roughly this order - and only after you are fairly comfortable with both linear algebra concepts (mainly vectors/matrices math - dot product and the like) and stats/probabilities. To a certain extent (and I have found this out with this current Udacity course), having a knowledge of some basic calculus concepts (derivatives mainly) will be of help - but so far, despite that minor handicap, I've been ok without that greater knowledge - but I do intend to learn it:

1. Coursera ML Class 2. Udacity AI Class 3. Udacity CS373 course 4. Udacity Self-Driving Car Engineer Nanodegree

> Do you think the course prepares you enough find a Self-Driving developer job?

I honestly think it will - but I also have over 25 years under my belt as a professional software developer/engineer. Ultimately, it - along with the other courses I took - will (and have) help me in having other tools and ideas to bring to bear on problems. Also - realize that this knowledge can apply to multiple domains - not just vehicles. Marketing, robotics, design - heck, you name it - all will need or do currently need people who understand machine learning techniques.

> Would you learn enough to compete/work along side people who got their Masters/PhD in Machine Learning?

I believe you could, depending on your prior background. That said, don't think that these courses could ever substitute for graduate degree in ML - but I do think they could be a great stepping stone. I am actually planning on looking into getting my BA then Masters (hopefully) in Comp Sci after completing this course. Its something I should have done long ago, but better late than never, I guess! All I currently have is an associates from a tech school (worth almost nothing), and my high school diploma - but that, plus my willingness to constantly learn and stay ahead in my skills has never let me down career-wise! So I think having this ML experience will ultimately be a plus.

Worst-case scenario: I can use what I have learned in the development of a homebrew UGV (unmanned ground vehicle) I've been working at on and off for the past few years (mostly "off" - lol).

> Appreciate your input.

No problem, I hope my thoughts help - if you have other questions, PM me...

Re: TensorKart: self-driving MarioKart with TensorFlow

#70

Post by PeterisP »

tobilarscheid wrote: post #44
eli_gottlieb wrote: post #30

Personally, I'm just a little impressed that you can train an active agent to play a game using old-fashioned supervised learning on screen states and controller states rather than relying on "action-oriented" learning techniques like reinforcement learning, online learning, or even a recurrent model. It really shows how simple many control tasks actually are!

This is exactly what I wondered about. So what exactly is the function you are training for? Is it basically like "if the screen (showing the track) looks like this, apply these controls"?

An more accurate description of the function would be "given this picture of a screen, what is the most likely key my author was pressing in this situation" - no goals, no values, no optimization, but simply learning to imitate the actions performed by a human.

Coincidentally, one of the neural network components in AlphaGo did pretty much the same, i.e. attempted to guess what human player would usually play in this situation purely based on the image and nothing else.

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