New Gun

I've done some work on algorithms to give optimal firing power. My method itself seems quite effective for the most part except there are a few caveats:
* Optimizing for score, optimizing for survival, and optimizing for PL yield rather different results for bullet power. The score vs survival difference is obvious, but optimizing for PL vs score is also important because the risk/reward tradeoffs are different depending on the score thus far in the match.
* It depends on accurate numbers for hit rates. For this I've implemented hitrate calculations which factor out the impact of distance on hit rate, but being able to incorporate other aspects would be better.
* Ideally you ought to try to model the ''future'' hitrates, by observing how the hitrates are changing over the course of the battle, however because the amount of data is limited, this can be difficult. It may be best to assume the hitrates are following a certain curve until the battle shows otherwise.
* My modeling of outcomes at different bullet power values is easiest to implement as a [[wikipedia:Monte Carlo method|monte carlo simulation]] of the rest of the battle. Implementing this in a bot like I have in the past causes a dilemma, because with few iterations it's accuracy is very poor, but with many iterations it starts to use a very large portion of the bot's avaliable cpu time.
* One could approximate this modeling by curve fitting it, though that could get a bit messy because of how many input parameters influence it. In my next bot I probably will use a curve fitted approximation of my bulletpower modeling.

Part of what really has me feeling good about accurate modeling of outcomes caused by bullet power, is that my implementations have had emergent behavior which I didn't expect at all but makes sense in retrospect. For example, it turns out that if it's almost 100% certain that the opponent will win a particular round, it can be worth it to dump remaining energy into high power bullets to deny the enemy bullet damage score.