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20 Feb 2018: function,Q(b, a) GP (0, k((b, a), (b, a))) wherethe kernelk(, ) is factored into separate kernels over thesummary state and action spaceskB(b, b)kA(a, a). ... 24, no. 4, pp. 562–588, 2010. [10] M. Gǎsić, S. Keizer, F.

20 Feb 2018: The summary action kernel is defined as:. kA(a,a′) = δa(a. ′) (3). ... 24. Figure 13: The number of times each system queries the user for feedback during on-linepolicy optimisation as a function of the number of training dialogues.

20 Feb 2018: Thomson and Young2010] Blaise Thomson and SteveYoung. 2010. Bayesian update of dialogue state:A pomdp framework for spoken dialogue systems.Computer Speech and Language, 24:562–588. ... Zhang and Chaudhuri2015] Chicheng Zhang and Ka-malika Chaudhuri.

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20 Feb 2018: To obtain a closed formsolution of (24), the policy π must be differentiable with respect to θ. ... 10. To lower the variance of the estimate of the gradient, a constant baseline, B, can beintroduced into (24) without introducing any bias [22].

20 Feb 2018: An advantage of this sparsification approach is that itenables non-positive definite kernel functions to be used in theapproximation, for example see [24]. ... It has already beenshown that active learning has the potential to lead to fasterlearning [24]

13 Mar 2018: t=20. t=22. t=24. t=26t=28Probabilistic. Space Carving. Error (RGB distance). Per. cent.

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13 Mar 2018: t=20. t=22. t=24. t=26t=28Probabilistic. Space Carving. Error (RGB distance). Per. cent.

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