Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling dynamical systems without requiring models of the system dynamics.

Recently, the Deep Learning community has become interested in evolutionary optimization (EO) as a means to address hard optimization problems, e.g.

This appendix contains additional material for the paper "Optimistic tree search strategies forblack-box combinatorial optimization".

A very promising direction in the field of black-box adversarial attacks are randomized search schemes for crafting adversarial examples [1, 23, 24]. Combining random search with specific update proposal distributions allows to achieve state-of-the-art black-box efficiency for different threat models such as` and `2 [1], `1 [25], `0, adversarial patches, and adversarial frames [24].

Moreover,tofurther accelerate overgreedy descent methods, wepresent a new accelerated random search (ARS) algorithm that incorporates prior information, together with aconvergence analysis.

The red lines in the bottom plot indicate linear fits and the red axis labels show the rank correlation coefficients ρ and p values. The matrix is orthogonal, thus avoiding a singular design. As scGen returns corrected input data, we performed PCA on the output data, which were used for further evaluation (cf. Appendix Section A.1). Here, we used the same number of principle components (PCs) as used for Embedded cells are colored by dataset. In Figure 9, we present the results of the simulation experiments discussed in the main text.

Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling dynamical systems without requiring models of the system dynamics.