Difference between revisions of "Raven"

Latest revision as of 10:54, 2 August 2021

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

RTune: RTune is a tuning system that uses Gradient Descent to optimize the weighting parameters; It has been written in Rust in order to minimize the time spent parsing the files and using kd-trees; The system starts by weighting every parameter 1.; Then it goes through the pre-recorded battles, dynamically constructing the kd-tree.; Unlike BeepBoop's training method, RTune uses the weights during search as well rather than only using it for distance.; After finding K nearest neighbors, the system weights each match by its distance to the search point and passes it through softmax in order to make sure the sum is 1.; The loss is calculated by 1 - sum of the weights that hit the enemy robot.; Despite not matching the performance of Raven 3.57 due to lost/extra code, Raven 3.59 managed to gain 0.5 APS over the 3.58 version with a training time of 80 seconds.

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.

@@ Line 59: / Line 59: @@
 : Add a flattener that actually improves its scores against adaptive targeting.
 : Improve the pytorch tuned targeting system
+; RTune
+: RTune is a tuning system that uses Gradient Descent to optimize the weighting parameters
+: It has been written in Rust in order to minimize the time spent parsing the files and using kd-trees
+: The system starts by weighting every parameter 1.
+: Then it goes through the pre-recorded battles, dynamically constructing the kd-tree.
+: Unlike [[BeepBoop]]'s training method, RTune uses the weights during search as well rather than only using it for distance.
+: After finding K nearest neighbors, the system weights each match by its distance to the search point and passes it through softmax in order to make sure the sum is 1.
+: The loss is calculated by 1 - sum of the weights that hit the enemy robot.
+: Despite not matching the performance of Raven 3.57 due to lost/extra code, Raven 3.59 managed to gain 0.5 APS over the 3.58 version with a training time of 80 seconds.
 ; Does it have any [[White Whale]]s?