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Say you have a bandit problem where the only feedback is

a) the number of times you pulled arm A

b) the number of times you pulled arm B

c) the sum total reward associated with A and B

on a daily basis. What strategy would you use to obtain the best policy here? I was thinking of just training a linear model where A and B are the inputs and C is the output - this weights would tell us the rate of reward for each arm.

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  • $\begingroup$ If the bandit arms are deterministic, can’t you just figure out the arm values after two days of data? E.g. two equations for two unknowns in terms of total reward. $\endgroup$ – Alex R. Jul 16 '19 at 23:51

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