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Quote: "Driving a new (untrained) section of the Royal Raceway:" So the author did a proper test of the model by scoring it on an unseen track to make sure it generalizes! This is very awesome!
How did we get from "bare minimum sensible testing" to "This is very awesome!"? Are things that bad on average?
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How did we get from "bare minimum sensible testing" to "This is very awesome!"? Are things that bad on average?
There's probably a broad range of people in the hn community.
NB: generalization should be one of, if not the first thing you think about and plan for in any ML project.
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!
In contrast, here's what is effectively an oracle machine playing mario kart: https://www.youtube.com/watch?v=ZBNgbJ5hXtQ (Amazingly detailed) info: http://tasvideos.org/5243S.html
I like how it just glitches itself to an almost instant win on half of the maps.
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I would love to learn how to do that - any suggestions? EDIT: to clarify: what should I google for?
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.
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I like how it just glitches itself to an almost instant win on half of the maps.
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.
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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…
A bit off-topic, but does anyone know how good this[0] course is? [0] https://www.udacity.com/course/deep-learning--ud730
i would recommend the coursera course by andrew ng. i had an amazing time. the code stays out of your way and he walks you through the algorithms and explains the theory very well.
i just started the fast.ai by jeremy howard, and literally have been blown away but the course. it is AMAZING! by lesson 3 i'm able to build cnn models and score on top 20% in kaggle competitions. not bad for a complete novice. HIGHLY RECOMMENDED.
once im done with the fast.ai course i may look back around to google's deep learning course. i think it may be easier for more experienced users to digest its info.
https://www.coursera.org/learn/machine-learning
Edit: added fast.ai link
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Step 1 is to make the AI find an ideal path through the course. Step 2 is to make AI figure out how to return to the ideal path through the course when other people are stealing your items or shelling you. step 3 is to make the AI figure out how to counter attack to slow down the opponents. Step 4 is OH GOD WE TAUGHT THE AI HOW TO ATTACK RUN FOR YOUR LIVES.
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.
But yes. point being, self driving is a feat of it's own, competing with opponents is a whole different ballgame with it's own set of challenges.
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"?