Congratulations on 100% PWin!

I’ve been long wondering where the bar of top bots are, now BeepBoop proved that there’s still much to do here! Congratulations on the surprising result! May be I could start a new bot as well soon!

For me it is not the 100% win, others achieved that too over time. It is the very dominant win, only 2 bots score better than 40%, and the gigantic survival, only 1 bot survives 30%. I have experimented with bulletpower, but never managed to get a real advantage with it. It seems however that the aggressive conservative way (whats in a name) of BeepBoop does pay off.

Yeah, after this one I will do an experimental release with more standard energy management to see how much BeepBoop's is making a difference.

My experience is that everything is dependent, score is some "multiplication" of everything. With top class guns and top class wave surfing, tuning energy management gives a lot gain when it isn't done right, so does tuning bandwidth, bullet shadow, etc. But with different guns, and different wave surfing algorithms, the optimal of everything else is different, so past tuning gets outdated very soon.

Anyway, the most gain always come from: 1. Fixing some significant weakness 2. Adding some big feature, e.g. Wave Surfing 3. Getting everything in 1 and 2 right.

Aye! Nicely done Kev! Seeing some of this sort of activity and similar kinda makes me feel tempted to come back to Robocode some time :)

Thanks everyone :). My theory is that "optimal" bots would be so good at dodging that there's nothing better than random targeting (taking into account walls, game physics, etc) against them. Certainly as bots get better hit rates go down, but I think there's a long way to go before bots are at that point.

DrussGT has a random gun as backup just in case some optimal bot does get invented ;) Unfortunately, it seems it has a weakness in the bullet power selection. I'll have to take a look at that...

Looking at DrussGT's virtual gun scores, the random gun does surprisingly close to the other ones against top surfers! The learned weights for BeepBoop's anti-surfer gun (1) put a lot of emphasis on wall features, (2) put basically no weight on historical features like time since decel, and (3) puts basically no weight on recency of the wave. This makes me wonder if BeepBoop's anti-surfer gun mostly acts as a slightly better random gun that is better at handling walls rather than something that learns patterns in the other bot's movement.

Well, DrussGT's random gun does take maximum reachable angles into account, which is probably 80% of wall effects. However there's probably something there about bots being hesitant to get closer to the enemy, even if they could potentially reach a further angle, which skews the distribution away from the reachable angles which are affected by walls. It would be interesting to do something with this hypothesis, but for me the random gun is really there as an emergency backup against someone simulating DrussGT's gun or something similar.

I had some strange experiment result. My guns are constantly doing worse than random against top surfers (oh no), but whenever I switch to some real random gun, it only decreased my score. Maybe a learning gun gives better bullet shadow? There's little study how guns affects bullet shadowing since its passive. Maybe adding some "active" thing to random gun helps.

Wow, you got the point! Random gun is better because they don’t suffer from bullet collision — but they do suffer from it when actually used. Since bullect collision can’t be simulated, the only way is by measuring hit rate without collision, which is a little biased since different gun may have different collision rate.

And I guess BeepBoop's AS gun works pretty well not because its some better random, but rather most surfers have significant weakness near walls. They generally weight distance very high to prevent getting too close (future risk), but essentially making them weak at dodging bullets near this scenario. Some repeated patterns exist because they learned the hit but still get to the same position.