what's the secret to making a good robot in robocode
No, because bearing offsets have no relationship to MEA. I think that raw MEA is the source of the orbit assumption in most guns. The raw MEA is assuming that the enemy can reach all those possible future locations by the time the wave hits, which it can't if it isn't orbiting. Precise MEA pretty much solves this.
Why is that an assumption they will ever go there? It's only taking into account that they could. If they never go there, what's the difference? If your bot has a min GF of -0.1 and a max of 0.3, it wouldn't make my gun any more accurate to change my MEA to match.
If an enemy bot never went outside of the range between -0.1 and 0.3, you're right, it probably wouldn't affect accuracy much. However, say an enemy bot has a huge spike at 1.0, and you decide to fire at it, even though the enemy cannot possibly reach 1.0 with its current heading. The shot would be wasted.
So, yeah, I'm always thinking in terms of precise MEA, and thinking that I will never fire at a GF the enemy never visited. So I don't see how it's assuming anything about how much of the escape range they might actually cover - the MEA is effectively however much of the range they ever cover. There is the scaling, which I think only assumes they're not moving in a fixed pattern.
And you're right that that is a problem with a certain MEA approximation formula some people use with their GuessFactors. :-)
GFs adjust angles on bullet flight time. If it saw GF 1.0 previously with a low powered bullet, when shooting with a high powered bullet, GF assumes the target will continue moving until it reaches the adjusted GF 1.0. This holds true only with orbital movement, or linear movement if close to walls.
If it was a pattern movement, like MyFirstRobot or SpinBot, the assumption would be wrong. But since pattern movement is clearly weaker than orbital movement, it is quite safe to assume opponents are orbiting. Segmenting by bullet power, distance and time-since-direction-change fixes the lack of accuracy in the long run.
But if you are trying to maximize APS by crushing weak movements, then PIT might be better in some situations.
If the enemy pays no attention to where I am and just moves randomly, the lateral displacement will still scale by BFT and GFs will still make sense, unless their algorithm is hard-coded to only move a fixed distance. That movement isn't orbital. Am I wrong?
Ok, any movement which scales with BFT works with GF. But which movements are these?
- Orbital movement
- Random movement normalized to move to all reachable angles (random orbital movement)
It doesn't work as well on other movements(slower learning):
- Pattern movement (including linear movement and oscillators)
- Random movements, which change heading/velocity based on anything not related to MEA
In practice, GF works against all movements above, but thanks to segmentation. Segmenting by distance and bullet power fixes most assumptions in GF which happen to be wrong in the long run.
Assuming wrongly that a movement is orbital is not that bad. Movements which do not try to maximize MEA are more predictable in the long run. If it is orbital, GF is right from the beginning and do well. If it is not orbital, then any statistical gun will have a higher hit rate in the long run.