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How about the same problem, but with each Jar having: a) a known fail percentage - 40% of the time the Jar fails and produces nothing. Maximize expected win. b) an unknown fail percentage, evenly distributed between 0 and 100%. Find a strategy that maximizes expected win over many runs (each run has new fail probabilities), by perfectly balancing between exploration of jars and exploitation. If you can find an optima…
Under 1 sec is amazing. When you say brute force, do you mean that for the first step (TS 1), you took all the three routes available, [0,0], [1,0], [2,0] for jar 1 and 2. Cause that just makes the number of branches too many to be solved in a reasonable amount of time. I would love to have a peek at you code.
The code is ugly and undocumented. I think the same could be accomplished in less than 10 lines of Haskell :). http://pastebin.com/MfXK9fwS
An implementation detail is that my branches are actually "jar1" "jar2" and "stepforward". To use a jar many times, you do jar1, jar1, stepforward.
Earlier quoted context omitted.
Under 1 sec is amazing. When you say brute force, do you mean that for the first step (TS 1), you took all the three routes available, [0,0], [1,0], [2,0] for jar 1 and 2. Cause that just makes the number of branches too many to be solved in a reasonable amount of time. I would love to have a peek at you code.
It is real brute force, but I do not investigate some stupid nodes. If I choose to not use an available jar during a timestep, I disqualify that jar, until another jar has been used, since there is no point in waiting to use a jar unless you want to save your balls for another jar. The code is ugly and undocumented. I think the same could be accomplished in less than 10 lines of Haskell :). http://pastebin.com/MfXK9f…