Difference between revisions of "Raven"

From Robowiki
Jump to navigation Jump to search
(Add details about the current PyTorch Tuner)
(Update to Raven 3.59's tuning method)
 
(One intermediate revision by the same user not shown)
Line 60: Line 60:
 
: Improve the pytorch tuned targeting system
 
: Improve the pytorch tuned targeting system
  
−
; Pytorch Tuner
+
; RTune
−
: The current tuning system is very naive and rather experimental.
+
: RTune is a tuning system that uses Gradient Descent to optimize the weighting parameters
−
: The formula used for transforming the data points is ax + bx^2
+
: It has been written in Rust in order to minimize the time spent parsing the files and using kd-trees
−
: For each datapoint, Guess Factor pair, it finds the K closest and K furthest Guess Factors in the given match and saves corresponding weights.
+
: The system starts by weighting every parameter 1.
−
: Then the transformer is trained to minimize (NN(input) - NN(kClosest))^2
+
: Then it goes through the pre-recorded battles, dynamically constructing the kd-tree.
−
: The obvious flaw with this system is that the optimal solution would be making all weights 0.  
+
: Unlike [[BeepBoop]]'s training method, RTune uses the weights during search as well rather than only using it for distance.
−
: This is prevented in a rather inelegant way:
+
: 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.
−
: All the A terms are normalized so that the sum of their absolute values is 1
+
: The loss is calculated by 1 - sum of the weights that hit the enemy robot.
−
: The B terms are clipped so that they can't be smaller than 0 so they can only increment the weights
+
: 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.
−
: This also makes sure that all transformations are one to one formulas(for the better?).
 
−
 
 
−
: It is important to normalize the transformer weights and the outputs as the optimal solution to this kind of problem is returning the smallest number possible.
 
  
 
; Does it have any [[White Whale]]s?
 
; Does it have any [[White Whale]]s?

Latest revision as of 10:54, 2 August 2021

Background Information

Bot Name
Raven
Author
Dsekercioglu
Extends
AdvancedRobot
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.

Strategy

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 fire?
It uses GuessFactor with KNN.
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.

Additional Information

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.
Retrieved from "http://robowiki.net/w/index.php?title=Raven&oldid=56947"