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18 Nov 2019: Therefore, we obtain another GP,. g(x) GP(h(x)T b,k(x, x′) h(x)T Bh(x′)). ... Preliminary Result. (a). (b)Figure: Predictions made after 1 (a) and 10 (b) iterations.

30 Oct 2019: Vπ(b) = Eπ{Rt | bt = b}=. b′p(b′ | b,π(b))[r(b,a)Vπ(b′)]db′ (2.12). ... Qπ(b,a) = Eπ{Rt | bt = b,at = a}=. b′p(b′ | b,a)[r(b,a) Qπ(b′,π(b′))]db′ (2.13).

30 Oct 2019: Algorithm 3 Off-policy Monte Carlo control1: Initialise Q arbitrarily, N(b,a) = D(b,a) = 0 b B,a A π. ... 7: Choose a′ ε -greedily8: Q(b,a) Q(b,a) α(r γ Q(b′,a′)Q(b,a))9: b b′,a a′.

18 Nov 2019: ba. ba. k′(x,x′)dxdx′ =. { b. b. k′. } 2. { a. ... b. k′. }. { a. a. k′. }= k(b,b) 2k(a,b) k(a,a).

18 Nov 2019: 62A.2 Line Search. 63A.3 zoom. 63. Appendix B Smoothing Spline 65. ... andβ N (b,B) are additional parameters (as mentioned in Section 2.5.3).

30 Oct 2019: kB(b,b′)kA(a,a. ′) (2.17). The prior for the residual follows Q(b,a) N(0,σ2). ... k((b,a), (b′,a′)) = 〈b,b′〉δa(a′) (2.19). The linear kernel assumes the elements of the space are features.

6 Nov 2019: Vπ(b) = a. π(a,b)b′. r. p(b′,r|b,a)(r γVπ(b′)) (2.8). relating the value function of state b to the value function of its ... 7: Q(b,a) Q(b,a) α[r γ max′a Q(b′,a′)Q(b,a)]8: b b′. 9: until beliefstate b is terminal10: until convergence

6 Nov 2019: Define a new kernel on the interval [a,b] by,. k̃(M)(x,x0) = (b a)M. ... or sin(wmx)) with wm harmonic on the interval [a,b], and suggested Fourier analaysis as amotivation for these features.

30 Oct 2019: 1. KM. at(. 0, x). Matérn kernel with = 5/2. (b) Matérn covariance function. ... a) Full GP model. (b) Sparse GP model. Fig. 2.3 Comparison of full GP and sparse GP models.

30 Oct 2019: k(b, b) = σ2(b b)2. which has been shown [29] to be able to approximate any arbitrary continuous function2. ... Xu(b) = maxau. R(b, a) γP(b|b, a) maxu. Xu(b) (3). where u u1,.