18.657: Mathematics of Machine Learning
نویسندگان
چکیده
3.1 Setup The stochastic multi-armed bandit is a classical model for decision making and is defined as follows: There are K arms(different actions). Iteratively, a decision maker chooses an arm k ∈ {1, . . . ,K}, yielding a sequence XK,1, . . . ,XK,t, . . ., which are i.i.d random variables with mean μk. Define μ∗ = maxj μj or ∗ ∈ argmax. A policy π is a sequence {πt}t≥1, which indicates which arm to be pulled at time t. πt ∈ {1, . . . ,K} and it depends only on the observations strictly interior to t. The regret is then defined as:
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تاریخ انتشار 2015