From the paper:I am also wonder how this AI would perform on a track with other cars, does it have any racing etiquette? Will it occasionally make mistakes?
Also the goal of the Gran Turismo AI has never been and will never be to be as fast as possible, it's only meant to give a relatively easy to overcome challenge.Looks interesting but remember this is using an off board computer to control one AI, you need to squash all that code into the game and then make it control 20 individual cars all at the same time.
It’s a start though, if the A.I can learn from your driving and pace it’s self against the player, it could make off line racing more interesting.Also the goal of the Gran Turismo AI has never been and will never be to be as fast as possible, it's only meant to give a relatively easy to overcome challenge.
My buddy sent me this. SUPER interesting.
It's only a travelling salesman problem if you decide to brute force it, which would be the worst possible way to build a functioning driving program.Going to reach into my back pocket and go quantum... At the end of the day it is a "traveling salesman" problem (NP-complete) which is why it was interesting for the paper's authors. The program/computer needs to go through every permutation of how to take each corner and straight with all of the speed variables, then plug each of those outputs into the following corner/straight, and so on. Very easy for a computer to do at first; however, as you add more corners, elevation, gearing, straights, braking, etc. the number of possible combinations quickly escalates into the trillions and beyond, becoming very difficult/impossible for traditional computers to resolve. As such, solving problems like this without having to resort to time consuming brute force attacks are at the forefront of quantum computing (similar in many ways to cryptography).
It's only a travelling salesman problem if you decide to brute force it, which would be the worst possible way to build a functioning driving program.
How would you even build a verification function for a fastest lap?If you want to achieve the "fastest" time/optimal solution then it is a NP-Complete problem. If you only want to make the AI achieve a "fast" time then there are other ways to get there. Remember, the NP-Complete designation is b/c it takes less time for the computer to verify the solution than it took for it to originally calculate/arrive at the solution.
Eh, some of those things are going to be easier for a computer to handle given their higher sensitivity.Sure, for a single time trial lap. We can make a computer go faster. But that’s easy. Now try and make it do a 24hr race at The Ring with a field of 200+ cars. The situational awareness, feel for tires/changes through a race. That’s the hard part.
No, verification is confirming that a solution is correct. How do you propose to verify that a given lap is the fastest possible lap?Verification is just running the ai output lap and comparing against other times. Same as traveling salesman. That’s why it’s a beast of a problem.
https://math.stackexchange.com/ques...-verification-for-travelling-salesman-problemNo, verification is confirming that a solution is correct. How do you propose to verify that a given lap is the fastest possible lap?
Eh, some of those things are going to be easier for a computer to handle given their higher sensitivity.
It's going to be able to accurately measure exactly how effective the brakes are, how much the tires slip due to wear, etc. and be able to perfectly hit brake points and turn-in points the same way each lap which means it'll be able to calculate how to adjust for worn tyres and so forth.
Situational awareness down the track is going to be worse though, that's true. It won't be able to look far down to the next corner through traffic and see the spin that happened there and prepare for it. It won't be able to judge that the cars going two-wide for the next corner aren't going to make it. It won't take personalities into account, e.g. "the #23 car always goes for the aggressive inside pass".
Humans are better at that stuff. Humans are also better at making worse-is-better decisions like staying out one or two laps on worn tyres and running suboptimal laps on a gamble to come out ahead of an opponent after the pitstop. Or run fewer laps on the fresh tyres in the second stint hoping to push harder then.
AI is more likely to pick the optimal solution each time which might hurt it over a race.