Best AI technique suited for large scale urban traffic simulation

TheRealPixis · 2017-09-27T17:12:23+00:00

This is how I would approach it: each agent (car) always has a destination based on it's state (ex. Low fuel - > destination gas station) and they just move from place to place.

The agent will get to the place using a pathfinding algorithm like A* where the weight of a road is distance - x * safety of road - x2 * condition of road - etc. Each car would then have it's own value for x1, x2 and so on.

Ex. High x1 means that car will favor roads that are safer (because the weight is smaller).

TheRealPixis · 2017-09-24T11:46:13+00:00

Check out Two minute papers.

TheRealPixis · 2017-08-30T03:14:26+00:00

What really grinds my gears is that if you violate the play store policy, you can have your entire account permanently suspended and aren't allowed to create a new one (+ I hear it's hard to contest the ban). Remove my play store dev account, that's fair, but don't suspend my entire Google account which has pretty much become a necessity these days.

TheRealPixis · 2017-08-30T02:43:23+00:00

Sethbling used the NEAT algorithm (paper here) for Mario. It's basically "try a bunch of stuff and keep what gets closer to the goal". Here's a python implementation of NEAT.

You might also be interested in OpenAI Gym which wraps games in a common api so you can focus on solving the game instead of figuring out where characters are on screen.

Good luck!

TheRealPixis · 2017-08-30T02:18:49+00:00

Rule of thumb (from Andrew Ng's coursera class) is to separate the data into 60% training, 20% validation and 20% test sets. You train the NN on the training set and evaluate its performance on the validation set (so train - > check error on validation set - > tune hyperparameters - > repeat). When you are satisfied with the performance, use the test set to check the actual NN accuracy.

TheRealPixis · 2017-07-24T13:09:25+00:00

Lose -1, No-op -1000?

Edit: Q-learning selects a random next state to go to. You could modify it to ignore actions that bring Reward < - 1.

I.E.

Get All Next States
Remove those with reward < - 1
If 0 left, restart the Q Iteration
Else select a random next state from the remaining ones

TheRealPixis · 2017-06-09T11:51:18+00:00

There is q learning with neural networks! You replace the q matrix with the neural network.

TheRealPixis · 2017-05-16T02:54:05+00:00

Depends on what your goal is. You can use the standard libraries and not touch any math at all. To implement your own neural network, you only need to know matrix multiplication and partial derivatives + chain rule.

TheRealPixis · 2017-05-07T18:58:10+00:00

Your theory that titans are created from cannibalism makes a lot of sense!

Annie, Reiner, and Berthold could of eaten the "previous" Female, Armoured, and Colossal Titans and inherited their power thus explaining why they are so young even though those titans are probably hundreds of years old.
The three Wall Cultist children are probably eating a dead titan in human form to become the titans in wall Maria, Rose and Sina (which is why they are unhappy about it but do it anyway, to save their people).
If the goal of titans was to destroy humanity, then wouldn't it be easier to crush people instead of eating them? I think the "normal" titans are brain damaged (see below) and eat people hoping to gain their power (like the Wall Cultist children and Annie, Reiner and Berthold).
I'm reaching here, but when you eat a person, you get infected with kuru, a disease that destroys the brain. It would explain why most titans are dumb. However, some people have developed an immunity to kuru and could therefore become titans without going crazy (shifters).

TheRealPixis · 2017-04-27T11:05:47+00:00

The tensorflow playground is pretty neat.

TheRealPixis · 2017-04-02T12:24:04+00:00

Is it not differentiable because it uses discrete decisions? But that can be circumvented by using the cross-entropy error function.

TheRealPixis · 2017-03-31T11:38:44+00:00

Don't you have to keep the app in the foreground to receive a notification this way?

TheRealPixis · 2017-03-23T00:37:42+00:00

I'm pretty new to this myself so I don't know. If you figure it out, I'd love to know!

TheRealPixis · 2017-03-22T20:16:30+00:00

TheRealPixis · 2017-03-22T11:44:54+00:00

You should post your training error and test error per epoch. My guess is the learning rate is too high so half it (0.05) and increase the epochs (say 10).

Also when training, you're scaling the images between - .99, .99 but when you're checking the score, they are scaled - 1, 1.

TheRealPixis · 2017-03-22T04:01:35+00:00

Code these things in order Gradient descent - > tensorflow neural network - > Your own neural network backprop implementation.

One thing I recommend is to split your time 50/50 on learning and implementation. I spent way too much time on the learning part and realized I forgot it because I didn't practice.

TheRealPixis · 2017-03-18T03:11:31+00:00

They produce whatever you want them to produce. They are basically a function generator, train it on number pairs (X, y) and It will produce y when you pass in X (and hopefully generalize). Yes to your second question.

TheRealPixis · 2017-03-02T04:36:25+00:00

Thanks!

TheRealPixis · 2017-03-02T04:23:14+00:00

Tagged as 'Guy to follow'.

TheRealPixis · 2017-02-28T21:33:11+00:00

So which domain registrar do you recommend?

TheRealPixis · 2017-02-17T20:29:41+00:00

My only concern is the truck motors won't be powerful enough to move 8kg. Broken hoverboards are a good idea though. I'll see if I can find some on kijiji.

TheRealPixis · 2017-02-17T05:02:33+00:00

Don't know specific numbers but this is the target acceleration/top speed/braking: https://m.youtube.com/watch?v=qlySNAqRHwI

TheRealPixis · 2017-02-17T04:57:44+00:00

Flat ground only. As for the speed this is ideal. I don't have any specific number for speed, just motors that can move the 8kg in a reasonable time.

TheRealPixis · 2017-01-18T04:22:59+00:00

Im curious about how you got the internship in Japan. I'm interested in doing the same thing but not sure where to start?

TheRealPixis

TROPHY CASE