SleepSiphon
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Background Information
SleepSiphon | |
Author(s) | Origin |
Extends | AdvancedRobot |
Targeting | Virtual Guns:
|
Movement | Minimum Risk Movement, Avoids: |
Current Version | 1.7b |
Code License | ZLIB |
- Sub-pages:
- Version History
- What's special about it?
- It is my first Robocode robot. Won first place in my school's competition.
- Great, I want to try it. Where can I download it?
- You can download SleepSiphon here!
- How competitive is it?
- N/A (Awaiting RoboRumble results.)
Strategy
- How does it move?
- It uses a version of Minimum Risk Movement. Generates destination points in a circle around itself. Calculates danger values for points in a straight line from its current location to that destination point based on a danger function. (Enemy Virtual Bullets data, distance from other enemies, distance from walls)
- How does it fire?
- It has four Virtual Guns: HOT, Linear Targeting, Circular Targeting, and Mean Targeting (Circular). Chooses the best gun for each enemy.
- Falls back to a (naive and inefficient) Displacement Vector-based KNN (not full Dynamic Clustering) gun if the virtual hit rate against an enemy is below a threshold.
- How does it dodge bullets?
- Enemy Virtual Bullets are a factor in its path-point danger function.
- How does the melee strategy differ from One-on-one strategy?
- It doesn't, currently.
- How does it select a target to attack/avoid in melee?
- Attacks whoever is closest.
- What does it save between rounds and matches?
- Between rounds, it stores information for the Displacement Vectors gun. Nothing between matches.
Additional Information
- Where did you get the name?
- This bot was an obsession of mine for a good 2 weeks... My sleep schedule suffered during that time...
- Can I use your code?
- Well, I won't stop you, but you really shouldn't. This is probably some of the most disorganized code you will ever read.
- Any use of my code must follow the ZLib License.
- SleepSiphon Github Repository
- What's next for your robot?
- Slight modifications to make it more effective in RoboRumble. Because this bot started as a high school project, I built it with the assumption that the other students' bots would follow set patterns & not learn. Obviously, this is not the case in RoboRumble, so some slight changes and optimizations can be made to my prediction. After that, however, I will be moving on to new robots.
- Does it have any White Whales?
- None presently.
- What other robot(s) is it based on?
-
- Inspiration for debugging graphics and "path risks" from Neuromancer
- "Never closest" movement, inspired by Shadow.
- I learned a lot about avoiding aggressive bots from playing against Bastion. (not on RoboWiki)