Mako

From Robowiki
Revision as of 03:44, 18 August 2017 by MultiplyByZer0 (talk | contribs) (Mass-edit Robocode Repository URLs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search
Sub-pages:
Version History
Mako
Author(s) PEZ
Extends AdvancedRobot
Targeting Angular Targeting, Averaged Bearing Offset Targeting
Movement Random Movement
Current Version 1.5
Download

Background Information

What's special about it?
At the moment I think the movement makes it special.

Strategy

How does it move?
It's following the strategy of giving the enemy as few clues as possible as to where it will go next. The tactics is to try flatten the movement curve. The technique is Random Movement.
How does it fire?
Two Virtual Guns:
  1. Factored Angular Targeting
  2. Averaged Bearing Offset Targeting
How does it dodge bullets?
It doesn't actively dodge bullets. It moves along the same strategy regardless if the enemy is firing or not.
How does the melee strategy differ from one-on-one strategy?
This is strictly a One on One bot.
What does it save between rounds and matches?
Between rounds it saves its Virtual Guns factors and factors for its two aiming methods. Between matches it saves nothing at the moment. (I'm pretty sure it should be an immediate improvement saving those factors persistently.)

Additional Information

Where did you get the name?
Mako is a mid sized shark. Possibly the fastest under water swimmer on earth. Highly effective as a predator and hasn't evolved for lots and lots of years. It's an ancient animal even for being a shark.
Can I use your code?
Not at the moment. I will eventually release the code for all my bots, but not while I am still competing. I'm more than happy to share ideas though.
What's next for your robot?
Dunno really. This bot is my test bed for ideas and stuff.
What other robot(s) is it based on?
Marshmallow (surprise!). Well, only some BotMath type of code really. Mako is the "farming team" of Marshmallow.
I also use Paul Evans' rolling averages function. Thank you Paul for sharing it!