TensorKart: self-driving MarioKart with TensorFlow (opens in new tab)

However, if you look at the code:

https://github.com/SullyChen/Autopilot-TensorFlow/blob/maste...

You can see that it follows much the same pattern as LeNet CNN for MNIST - a few (ok, more than a few!) convolutional layers followed by a few fully connected layers.

Maybe you could call it a "follow on" or perhaps an ANN pattern?:

Conv -> Conv -> Reshape/Flatten -> FC -> FC -> FC

(disregarding activation and such)

...which is really the lesson of the LeNet MNIST CNN - at least, that's my takeaway.

> 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...

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 you're strapped for cash and don't want to pay the $800/term, you could definitely learn these things on your own using free online resources. If you don't mind the price, though, I've found this course worth the time+money+effort so far. [they're not paying me to say this :)]

These are Tool-Assisted Speedruns. That means that it's a human player using things like slow motion, mem dumps and other mechanisms to play perfect games. It's more an example of human's abilities when augmented with computers than AI discovering those glitches itself.

Here's what I could find in a couple minutes:

https://github.com/openai/universe-starter-agent

OpenAI's example universe agent. Remember that while their goal is an agent that works in any and all environments (read: games), you could certainly optimize yours just for MarioKart.

Reinforcement Learning. Here's a good intro: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

There's probably a broad range of people in the hn community.

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.

In TFA it says that he was training a supervised learner to predict the control state from the screen state. So yes, "if the screen looks like this, apply these controls", and that can play Mario Kart 64.

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 this for a fact.

Step 2.5 would be to make the AI figure out how to evade or minimize the effect of or ability to initiate opponents' offensive moves. That would be the most interesting bit to me. Would be neat to see an AI intentionally stay in 2nd place with an item at the ready until the home stretch, to avoid being blue-shelled.