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Generalized TD Learning

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Figure

Figure 1: Graphical model for infinite horizon MRP. s and r denote state variable and reward, re-spectively.
Figure 2: An illustrative plot of 1/T ∑t f(xt,θ) as function of θ (solid line). Due to the effect offinite samples, the function is slightly apart from its expectation Eθ∗,ξ∗[ f(x,θ)] (dashedline) which takes 0 at θ = θ∗ because of condition (6)
Figure 3: A five-states MRP.
Table 1: Relation between the current learning and the proposed algorithms. I denotes an m × midentity matrix.
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