you speak really good english for someone whose first language is not english, but when you talk about how something looks, you don't say HOW something looks LIKE you say WHAT it looks LIKE. if you say HOW you never end the phrase with LIKE. great vid <33
This could be a fine addition to a duel 1v1 gamemode where you compete to be the fastest lap by 3 rounds race in a random generated map. Great work dude
Some of you may know, I even held a cup with 30 AI generated maps two months back. Here are the results - ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-GBLd9srf8r8.html
Well done! Very interesting to watch. Even though it would take much more computing time, wouldn't it make more sense, to get a probability by looking at segments of blocks? Like always 4 blocks at a time for example. Give it a start block. Now calculate a segment of 4 blocks, place the first of them. Now you have two blocks. Calculate first, second, and a theoretical third and fourth (judging by common 4 block patterns, with a start block attatched to the front of them). Place your third block. Now calculate your fourth block, by looking at common 4-block-combinations using the last 2 placed blocks. Place your 4th block. As far as I'm not missjudging it, the endresult should be smoother (even if you don't take the one with the highest probability, cause this would end in the same route every time).
What's the rationale behind randomly choosing outputs (weighted by probability) rather than choosing the most probable output every time? I can imagine choosing randomly would give more "natural" results in some sense, but would expect overall higher quality tracks from choosing the most probable since results would resemble the training set more closely. Just guessing though, I have little experience working with NNs.
Choosing the most probable output every time generates you the same track, every single time. How conservative we are towards choosing the output is controlled by the "temperature" hyper-parameter. If it's lower, the output will be more conservative, if it's higher - more diverse. For example, setting the parameter to a very low value, will generate mostly straight line tracks with only the same one block repeating. Of course there are many strategies you can take e.g changing the temperature while generating the track.
Why are we not using reinforcement learning or particle swarm based algorithms to create theoretical speedruns with AI? I kept looking for it and there are some initial works on it but with a legend like Donadigo on board, we might come up with AI speedruns.
awesome video! Now combine that with @Yosh 's TM driver AI to drive AI generated tracks. That way we have completely eliminated the human in the process and fully bowed down to our robot overlords.
So so cool ! greatjob An idea : maybe you could provide your neural network with a sense of the driving flow by providing it with the trajectory of a player for each block of the track (or at least, player's entry point & speed and exit point & speed for each of the block) Maybe with this kind of training data, the network would learn the correlation between common sequences of blocks and the expected trajectory and speed on those sequences ?
