Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains: Supplementary Material
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چکیده
We prove the consistency and coverage error for the bootstrapped studentized interval around our the sample mean of the sequence of parameters for global function approximation. Each parameter vector θt on time step t corresponds to the action-value functionQt on that time step, withQ(s, a) = f(θ, s, a) for some bounded function f . A common example of f is a linear function f(θ, s, a) = θφ(s, a) with features given by the function φ : S ×A→ R.
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تاریخ انتشار 2010