Here’s what I’m thinking: The neural network doesn’t have to be correct about which one you pick, it has to be correct about which you don’t pick. Only one option they pick is a loss, so if the other party can be somewhat certain you won’t pick a specific option, it can at least tie. So if a randomizer has pretty even distribution, I think it can win more than half the time, because it can gather roughly how likely the same choice is to be played in a row.
I’m basically suggesting a predictable distribution can be exploited in RPS.
Honestly, the bot could always be winning against the RNG by dumb luck. More experimentation would be needed to be sure. I am just making guesses.
> I think it can win more than half the time, because it can gather roughly how likely the same choice is to be played in a row.
With a random choice, the chance of playing the same choice in a row is 1/3. This does not give you any advantage over having no information (where each choice has a 1/3 chance.)
I think the misunderstanding is in
> a randomizer has pretty even distribution
Here's a thought experiment that might help: imagine what you say is truly the case - that would mean you could "charge up" a dice by rolling it until you got a long run of a given number - lets pick something arbitrary, say you roll until you get 5 twos in a row. According to what you've said the chance of the next number rolled being a two is now lower than it was when you started "charging up" your dice.
How is this possible? Nothing is physically changing about the dice between rolls.
> So if a randomizer has pretty even distribution, I think it can win more than half the time, because it can gather roughly how likely the same choice is to be played in a row.
I’m basically suggesting a predictable distribution can be exploited in RPS.
Honestly, the bot could always be winning against the RNG by dumb luck. More experimentation would be needed to be sure. I am just making guesses.