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20 Feb 2018: Q(b,a) GP (0,k((b,a), (b,a))) (2). where the kernel k(, ) is factored into separate kernels overbelief and action spaces kB(b,b′)kA(a,a′). ... kA(a,a′) = δa(a. ′) (7). where δa(a′) = 1 iff a = a′, 0 otherwise.

20 Feb 2018: k(b,a,b′,a′) = kB(b,b′)kA(a,a. ′). (3). In addition, if we assume additive noise in the Q-function, Q(b,a) N(0,σ2), the following ... kA(a,a′) = 1 δa(a′) (14). where δa(a′) = 1 iff a = a′.For the full-space systems, the kernel function

20 Feb 2018: 211. 2121. 1. aannna. annnnn. ka. rd (13). Here nr specifies the number of events that occurred r times and a fixed discounting factor was used if they are zero.

20 Feb 2018: kA(a,a′). For a training sequence of belief state-action pairs B = [(b0,a0),. , ... kA(a,a′) = δa(a. ′) (6). where δa(a′) = 1 iff a = a′, 0 otherwise.

20 Feb 2018: is factored into separate kernels over thesummary state and action spaces kC(c,c′)kA(a,a′).

20 Feb 2018: kA(a,a′). The. policy is optimised using an algorithm called GP-SARSA [7, 45] in which theQ-function is updated by calculating the posterior given the collected belief-action pairs ... The summary action kernel is defined as:. kA(a,a′) = δa(a. ′)

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).

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20 Feb 2018: Zhang and Chaudhuri2015] Chicheng Zhang and Ka-malika Chaudhuri. 2015. Active learning fromweak and strong labelers.

20 Feb 2018: Q(b,a) GP (0,k((b,a), (b,a))). (41). The kernel k(, ) is often factored into separate kernels over the belief state and actionspaces kB(b,