how to build a good test bed?

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It was read a line, add to tree, and if it was a firing tick do a prediction. For parallelization I just started a new thread for each bot, and join the thread when the bot is processed. It would probably br a bit faster with a thread pool.

Unfortunately I think I lost this code, I think it was on my University computer...

Skilgannon (talk)11:09, 5 June 2019

I'm even using thread pool & nio for potentially faster execution. Maybe 5000 roborumble battles should not take 3x time as 1500 tcrm battles, as the rumble contains a lot of easy targets which get destroyed in a second. I'll experiment later.

Btw, my crossover code is not simply doing some gradient descent, but rather do gradient descent or use the weight from one parent directly, based on some random process. Random noise is also added on small probability though. I think this process explores more possible searching space than simply gradient descent + random component. As my experience, the searching space of knn weights is non-trivial, although some pattern exists for most good weights.

Xor (talk)06:16, 6 June 2019
 

one more question: how many generations are you generally using?

For me, 10 generations produces result good enough, and increasing it further to 100 doesn’t improve much.

However, it seems that 1500 tcrm battles suffers from overfitting a lot, and I’m trying full rumble now.

Each time I collect data & do genetic tuning with 1500 tcrm battles, the hit rate increases from ~16% to ~17%, however actual tcrm score even decreases sometimes.

Xor (talk)09:59, 14 June 2019

It depended on the population size and the sampling strategy I used. If I used larger population and less converging sampling strategy then I could run up to about 100 generations before it would converge.

And I think the solution space is very non-convex with lots of local minima, I ran quite a few simulations and it converged to different solutions each time.

Skilgannon (talk)16:11, 16 June 2019
 
 
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