Raven

Background Information

What's special about it?: Raven uses a form of Go to surfing where it procedurally generates paths without aiming for any point.

Great, I want to try it. Where can I download it?: https://www.dropbox.com/s/ln53uvb3ddxe4bv/dsekercioglu.mega.Raven_3.56j8.jar?dl=1

How competitive is it?: Its best is 7th place but considering how close it is to Gilgalad, 7.5th would be the right word =)

Credits: Rednaxela, Skilgannon, Nat, Starrynyte and the other contributors I am unaware of for the FastTrig class.; Skilgannon for the bugless, fast Kd-tree.; Cb for the non-iterative wall smoothing.; Rozu for the precise prediction code.; Chase-san for the intercept method I used in my PPMEA calculations.; AW for giving me the idea of integrating the danger function in order to get the danger value for a given bot width.; Kev for inspiring me to use pytorch based on my loose estimate on how BeepBoop works.

How does it move?: A form of Go To Surfing.; It falls back to True Surfing when there is no bullets mid-air.

How does it dodge bullets?: Tries to minimize the number of guess factors it gets hit by based on their weights and damage.

What does it save between rounds and matches?: Between rounds, it saves the kd-trees. Between matches, it doesn't save anything.

Where did you get the name?: It just popped into my mind and I thought it would be a proper name for a bot with machine learning.

Can I use your code?: Yes, I tried to make the code as clean and understandable as possible.

What's next for your robot?: A proper versioning system so I don't keep accidentally releasing experimental versions.; Faster code so it doesn't run slow and doesn't skip turns.; Better bullet shadow calculations.; Tuning the guns since they haven't been tuned since the first version.; Gun Heat Waves.; Maybe a pre-trained movement or gun to use in the first ticks of the battle.; Add a flattener that actually improves its scores against adaptive targeting.; Improve the pytorch tuned targeting system

Pytorch Tuner: The current tuning system is very naive and rather experimental.; The formula used for transforming the data points is ax + bx^2; For each datapoint, Guess Factor pair, it finds the K closest and K furthest Guess Factors in the given match and saves corresponding weights.; Then the transformer is trained to minimize (NN(input) - NN(kClosest))^2; The obvious flaw with this system is that the optimal solution would be making all weights 0.; This is prevented in a rather inelegant way:; All the A terms are normalized so that the sum of their absolute values is 1; The B terms are clipped so that they can't be smaller than 0 so they can only increment the weights; This also makes sure that all transformations are one to one formulas(for the better?).

Does it have any White Whales?: Drifter has been crushing the latest versions.; Ever since I realized memory allocations and deallocations weren't free, the true White Whale is the Java GC :)

What other robot(s) is it based on?: It's kind of based on WhiteFang, I have tried to copy the design but make it as precise as it can be.