Difference between revisions of "User:Xor/Thoughts on movement"

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Apart from the raw data you've collected from game and [[Robocode/Game Physics|physics]], it's rather important to estimate the real risk.  
 
Apart from the raw data you've collected from game and [[Robocode/Game Physics|physics]], it's rather important to estimate the real risk.  
 +
 
A hit from [[GuessFactor Targeting]] will probably indicate that the bins nearby will also have relatively high score, thus being there will make them learn faster, therefore increase their hitrate. The same thing happens to fast learning guns as well, but not that significant, as they decay data very fast.  
 
A hit from [[GuessFactor Targeting]] will probably indicate that the bins nearby will also have relatively high score, thus being there will make them learn faster, therefore increase their hitrate. The same thing happens to fast learning guns as well, but not that significant, as they decay data very fast.  
Note that different guns may also aim from different positions, introducing deviation in their targeting. Then we have:
+
 
 +
Note that different guns may also aim from different positions, introducing deviation in their targeting.
 +
 
 +
Then we have:
 
* '''[[Kernel Density]]''':
 
* '''[[Kernel Density]]''':

Revision as of 03:50, 7 September 2017

Introduction

Robocode is just another name of movement, and modern movement is just another name of risk management.

Source of risk

To manage risk you need to first find them out, and there are mainly four types of risks in 1v1:

  • Hit Stats: A simple gun and a slow learning gun will probably fire at where they used to fire.
  • Flattener: A fast learning gun will probably fire at your most recent movement, and it's important to prelearn them to keep the hitrate relatively low.
  • Distancing: A random firing gun will hit you more at closed distance; At very closed distance, ram would also happen.
  • Bullet Shadow: Part of the risk from enemy firing can be nullified by your own bullet, as bullets can collide.

Risk estimation

Apart from the raw data you've collected from game and physics, it's rather important to estimate the real risk.

A hit from GuessFactor Targeting will probably indicate that the bins nearby will also have relatively high score, thus being there will make them learn faster, therefore increase their hitrate. The same thing happens to fast learning guns as well, but not that significant, as they decay data very fast.

Note that different guns may also aim from different positions, introducing deviation in their targeting.

Then we have: